├── .DS_Store ├── Agents ├── .DS_Store ├── DQN_Agent.py ├── DQN_Agent_LSTM.py ├── README.md ├── q_learning_agent.py ├── scripted_agent.py └── sentry_defense.py ├── CONTRIBUTING.md ├── ISSUE_TEMPLATE.md ├── Images ├── 2C01EB1027814BB7FF16A15272E1B2DEF9FDEEC3.jpg ├── Captura de pantalla 2017-09-03 a las 12.05.18.png ├── Captura de pantalla 2017-09-08 a las 17.05.07.png ├── Captura de pantalla 2017-09-08 a las 17.48.55.png ├── Captura de pantalla 2017-09-17 a las 12.58.11.png ├── Captura de pantalla 2017-09-17 a las 12.58.45.png ├── Captura de pantalla 2017-09-17 a las 13.09.44.png ├── Captura de pantalla 2017-09-18 a las 20.14.14.png ├── Captura de pantalla 2018-01-02 a las 14.51.09.png ├── Captura de pantalla 2018-08-13 a las 15.43.15.png ├── ForceField.png ├── ForceField2.png ├── HallucinIce.gif ├── HallucinIce.png ├── HallucinIceV2.png ├── README.md ├── Reward shaping.png ├── ScoreUpdateVariables.png ├── a0031372f617b683f02fe0ae163419ca-sc2-battlechest-product.jpg ├── change Marines.png ├── change marine.png ├── change sentry.png ├── change_units.png ├── change_units_new.png ├── new_init.png ├── print_screen_1.png ├── trigger.png └── zerling change.png ├── LICENSE ├── README.md ├── RL └── README.md ├── Replays ├── DefeatRoaches_2017-09-08-13-59-49.SC2Replay └── README.md ├── SentryDefense.SC2Map ├── _config.yml ├── docs ├── README.md ├── SentryUnit.md ├── Terran │ └── Terran_mini_games.md └── _config.yml ├── new_minigames ├── .DS_Store ├── BlueMoon │ ├── Images │ │ ├── First_Tech_Core_Protoss.png │ │ ├── Protoss.jpg │ │ └── README.MD │ ├── README.md │ └── bluemoon.SC2Map ├── FlowerFields │ ├── FlowerFields.SC2Map │ └── README.md ├── HallucinIceSMAC │ ├── HallucinIce.SC2Map │ └── README.md ├── MicroPrism │ └── README.MD ├── RedWaves │ ├── README.md │ └── redwaves.SC2Map ├── SentryDefense │ ├── README.md │ └── SentryDefense.SC2Map ├── SentryForceField │ ├── ForceField.SC2Map │ ├── README.md │ └── scripted_agent.py ├── SentryHallucination │ ├── HallucinIce.SC2Map │ ├── README.md │ └── scripted_agent.py └── TheRightPath │ ├── README.md │ └── TheRightPath.SC2Map ├── requirements.txt └── zip ├── README.md └── mini-games.zip /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/.DS_Store -------------------------------------------------------------------------------- /Agents/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/Agents/.DS_Store -------------------------------------------------------------------------------- /Agents/DQN_Agent.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import sys 3 | import random 4 | 5 | from keras.models import Sequential 6 | from keras.layers import Dense, Flatten, Conv2D, Activation, MaxPooling2D 7 | from keras.optimizers import Adam, Adamax, Nadam 8 | from keras.backend import set_image_dim_ordering 9 | from absl import flags 10 | 11 | from pysc2.env import sc2_env, environment 12 | from pysc2.lib import actions 13 | from pysc2.lib import features 14 | 15 | from rl.memory import SequentialMemory 16 | from rl.policy import LinearAnnealedPolicy, EpsGreedyQPolicy 17 | from rl.core import Processor 18 | from rl.callbacks import FileLogger, ModelIntervalCheckpoint 19 | from rl.agents.dqn import DQNAgent 20 | from rl.agents.sarsa import SARSAAgent 21 | 22 | ## Actions from pySC2 API ( Move, attack, select , hallucination actions ) 23 | 24 | _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index 25 | _PLAYER_FRIENDLY = 1 26 | _PLAYER_NEUTRAL = 3 # beacon/minerals 27 | _PLAYER_HOSTILE = 4 28 | _NO_OP = actions.FUNCTIONS.no_op.id 29 | _MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id 30 | _ATTACK_SCREEN = actions.FUNCTIONS.Attack_screen.id 31 | _SELECT_ARMY = actions.FUNCTIONS.select_army.id 32 | _NOT_QUEUED = [0] 33 | _SELECT_ALL = [0] 34 | _HAL_ADEPT = actions.FUNCTIONS.Hallucination_Adept_quick.id 35 | _HAL_ARCHON = actions.FUNCTIONS.Hallucination_Archon_quick.id 36 | 37 | ## Size of the screen and length of the window 38 | 39 | _SIZE = 64 40 | _WINDOW_LENGTH = 1 41 | 42 | ## Load and save weights for training 43 | 44 | LOAD_MODEL = False #True si ya está creado para entrenar el modelo 45 | SAVE_MODEL = True 46 | 47 | ## global variable 48 | 49 | episode_reward = 0 50 | 51 | ## Configure Flags for executing model from console 52 | 53 | FLAGS = flags.FLAGS 54 | flags.DEFINE_string("mini-game", "HalucinIce", "Name of the minigame") 55 | flags.DEFINE_string("algorithm", "deepq", "RL algorithm to use") 56 | 57 | ## Processor 58 | # A processor acts as a relationship between an Agent and the Env . 59 | # useful if the agent has different requirements with respect to the form of the observations, actions, and rewards of environment 60 | # How many frames will be an obs ? 61 | 62 | class SC2Proc(Processor): 63 | def process_observation(self, observation): 64 | """Process the observation as obtained from the environment for use an agent and returns it""" 65 | obs = observation[0].observation["feature_screen"][_PLAYER_RELATIVE] # Read the features from the screen . This will change with pix2pix 66 | return np.expand_dims(obs, axis=2) 67 | 68 | def process_state_batch(self, batch): 69 | """Processes an entire batch of states and returns it""" 70 | return batch[0] 71 | 72 | def process_reward(self, reward): 73 | """Processes the reward as obtained from the environment for use in an agent and returns it """ 74 | reward = 0 75 | return reward 76 | 77 | 78 | ## Define the environment 79 | 80 | 81 | class Environment(sc2_env.SC2Env): 82 | """Starcraft II enviromnet. Implementation details in lib/features.py""" 83 | 84 | def step(self, action): 85 | """Apply actions, step the world forward, and return observations""" 86 | global episode_reward # global variable defined previously 87 | 88 | action = actions_to_choose( 89 | action) # Actions of Hallucination and movement Make a function that selects among hallucination functions 90 | obs = super(Environment, self).step( 91 | [actions.FunctionCall(_NO_OP, [])]) ## change the action for Hallucination or attack ? 92 | # The method calls an observation that moves the screen 93 | observation = obs 94 | r = obs[0].reward 95 | done = obs[0].step_type == environment.StepType.LAST # Episode_over 96 | episode_reward += r 97 | 98 | return observation, r, done, {} # Return observation, reward, and episode_over 99 | 100 | def reset(self): 101 | # reset the environment 102 | global episode_reward 103 | episode_reward = 0 104 | super(Environment, self).reset() 105 | 106 | return super(Environment, self).step([actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL])]) 107 | 108 | 109 | def actions_to_choose(action): 110 | hall = [_HAL_ADEPT, _HAL_ARCHON] 111 | action = actions.FunctionCall(random.choice(hall), [_NOT_QUEUED]) 112 | return action 113 | 114 | 115 | ## Agent architecture using keras rl 116 | 117 | 118 | ### Model 119 | # Agents representation of the environment. ( How the agent thinks the environment works) 120 | 121 | #### 1. 256 , 127, 256 are the channels- depth of the first layer, one can be colour, edges) 122 | #### 2. Kernel size is the size of the matrix it will be use to make the convolution ( impair size is better) 123 | #### 3. strides are the translation that kernel size will be making 124 | #### 4. The Neural net architecture is CONV2D-RELU-MAXPOOL-FLATTEN+FULLYCONNECTED 125 | 126 | def neural_network_model(input, actions): 127 | model = Sequential() 128 | model.add(Conv2D(256, kernel_size=(5, 5), input_shape=input)) 129 | model.add(Activation('relu')) 130 | 131 | model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None)) 132 | model.add(Flatten()) 133 | model.add(Dense(actions)) # This means fully connected ? 134 | model.add(Activation('softmax')) 135 | 136 | model.compile(loss="categorical_crossentropy", 137 | optimizer="adam", 138 | metrics=["accuracy"]) 139 | 140 | return model 141 | 142 | 143 | def training_game(): 144 | env = Environment(map_name="HallucinIce", visualize=True, game_steps_per_episode=150, agent_interface_format=features.AgentInterfaceFormat( 145 | feature_dimensions=features.Dimensions(screen=64, minimap=32) 146 | )) 147 | 148 | input_shape = (_SIZE, _SIZE, 1) 149 | nb_actions = _SIZE * _SIZE # Should this be an integer 150 | 151 | model = neural_network_model(input_shape, nb_actions) 152 | # memory : how many subsequent observations should be provided to the network? 153 | memory = SequentialMemory(limit=5000, window_length=_WINDOW_LENGTH) 154 | 155 | processor = SC2Proc() 156 | 157 | ### Policy 158 | # Agent´s behaviour function. How the agent pick actions 159 | # LinearAnnealedPolicy is a wrapper that transforms the policy into a linear incremental linear solution . Then why im not see LAP with other than not greedy ? 160 | # EpsGreedyQPolicy is a way of selecting random actions with uniform distributions from a set of actions . Select an action that can give max or min rewards 161 | # BolztmanQPolicy . Assumption that it follows a Boltzman distribution. gives the probability that a system will be in a certain state as a function of that state´s energy?? 162 | 163 | policy = LinearAnnealedPolicy(EpsGreedyQPolicy(), attr="eps", value_max=1, value_min=0.7, value_test=.0, 164 | nb_steps=1e6) 165 | # policy = (BoltzmanQPolicy( tau=1., clip= (-500,500)) #clip defined in between -500 / 500 166 | 167 | 168 | ### Agent 169 | # Double Q-learning ( combines Q-Learning with a deep Neural Network ) 170 | # Q Learning -- Bellman equation 171 | 172 | dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, 173 | nb_steps_warmup=500, target_model_update=1e-2, policy=policy, 174 | batch_size=150, processor=processor) 175 | 176 | dqn.compile(Adam(lr=.001), metrics=["mae"]) 177 | 178 | 179 | ## Save the parameters and upload them when needed 180 | 181 | name = "HallucinIce" 182 | w_file = "dqn_{}_weights.h5f".format(name) 183 | check_w_file = "train_w" + name + "_weights.h5f" 184 | 185 | if SAVE_MODEL: 186 | check_w_file = "train_w" + name + "_weights_{step}.h5f" 187 | 188 | log_file = "training_w_{}_log.json".format(name) 189 | callbacks = [ModelIntervalCheckpoint(check_w_file, interval=1000)] 190 | callbacks += [FileLogger(log_file, interval=100)] 191 | 192 | if LOAD_MODEL: 193 | dqn.load_weights(w_file) 194 | 195 | dqn.fit(env, callbacks=callbacks, nb_steps=1e7, action_repetition=2, 196 | log_interval=1e4, verbose=2) 197 | 198 | dqn.save_weights(w_file, overwrite=True) 199 | dqn.test(env, action_repetition=2, nb_episodes=30, visualize=False) 200 | 201 | if __name__ == '__main__': 202 | FLAGS(sys.argv) 203 | training_game() 204 | 205 | -------------------------------------------------------------------------------- /Agents/DQN_Agent_LSTM.py: -------------------------------------------------------------------------------- 1 | #SC2-pySC2 agent for HallucinIce mini-game 2 | #@SoyGema 3 | #Thanks to @DavSuCar 4 | 5 | 6 | import numpy as np 7 | import sys 8 | import random 9 | import time 10 | 11 | from keras.models import Sequential 12 | from keras.layers import Dense, Flatten, Conv2D, Activation, MaxPooling2D, TimeDistributed, LSTM, Reshape 13 | from keras.optimizers import Adam, Adamax, Nadam 14 | from keras.backend import set_image_dim_ordering 15 | from absl import flags 16 | 17 | from pysc2.env import sc2_env 18 | from pysc2.env import environment as pysc2environment 19 | from pysc2.lib import actions 20 | from pysc2.lib import features 21 | 22 | from rl.memory import SequentialMemory 23 | from rl.policy import LinearAnnealedPolicy, EpsGreedyQPolicy 24 | from rl.core import Processor 25 | from rl.callbacks import FileLogger, ModelIntervalCheckpoint 26 | from rl.agents.dqn import DQNAgent 27 | # from rl.agents.sarsa import SARSAAgent 28 | 29 | # Actions from pySC2 API 30 | 31 | FUNCTIONS = actions.FUNCTIONS 32 | _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index 33 | _PLAYER_FRIENDLY = 1 34 | _PLAYER_NEUTRAL = 3 35 | _PLAYER_HOSTILE = 4 36 | _NO_OP = FUNCTIONS.no_op.id 37 | _MOVE_SCREEN = FUNCTIONS.Move_screen.id 38 | _ATTACK_SCREEN = FUNCTIONS.Attack_screen.id 39 | _SELECT_ARMY = FUNCTIONS.select_army.id 40 | _NOT_QUEUED = [0] 41 | _SELECT_ALL = [0] 42 | _HAL_ADEPT = FUNCTIONS.Hallucination_Adept_quick.id 43 | _HAL_ARCHON = FUNCTIONS.Hallucination_Archon_quick.id 44 | _HAL_COL = FUNCTIONS.Hallucination_Colossus_quick.id 45 | _HAL_DISRUP = FUNCTIONS.Hallucination_Disruptor_quick.id 46 | _HAL_HIGTEM = FUNCTIONS.Hallucination_HighTemplar_quick.id 47 | _HAL_IMN = FUNCTIONS.Hallucination_Immortal_quick.id 48 | _HAL_PHOENIX = FUNCTIONS.Hallucination_Phoenix_quick.id 49 | _HAL_STALKER = FUNCTIONS.Hallucination_Stalker_quick.id 50 | _HAL_VOIDRAID = FUNCTIONS.Hallucination_VoidRay_quick.id 51 | _HAL_ZEALOT = FUNCTIONS.Hallucination_Zealot_quick.id 52 | _FORCE_FIELD = FUNCTIONS.Effect_ForceField_screen.id 53 | _GUARD_FIELD = FUNCTIONS.Effect_GuardianShield_quick.id 54 | 55 | # Size of the screen and length of the window 56 | 57 | _WINDOW_LENGTH = 1 58 | 59 | # Load and save weights for training 60 | 61 | LOAD_MODEL = False # True if the training process is already created 62 | SAVE_MODEL = True 63 | 64 | # Configure Flags for executing model from console: 65 | 66 | FLAGS = flags.FLAGS 67 | flags.DEFINE_string("mini_game", "HallucinIce", "Name of the minigame.") 68 | flags.DEFINE_integer("screen_size", "64", "Resolution for screen actions.") 69 | flags.DEFINE_integer("minimap_size", "32", "Resolution for minimap actions.") 70 | # flags.DEFINE_string("algorithm", "deepq", "RL algorithm to use") 71 | flags.DEFINE_bool("verbose", False, "Intended to help a programmer read actions taken in real-time.") 72 | flags.DEFINE_float("pause", 0.0, "Seconds to pause between consecutive actions. Intended to help a programmer observe the effect of an action taken in real-time.") 73 | flags.DEFINE_bool("visualize", True, "Visualize game") 74 | flags.DEFINE_integer("steps_per_ep", 150, "steps per episode.") 75 | FLAGS(sys.argv) 76 | 77 | # Processor 78 | 79 | class SC2Proc(Processor): 80 | def process_observation(self, observation): 81 | """Process the observation as obtained from the environment for use an agent and returns it""" 82 | obs = observation[0].observation["feature_screen"][_PLAYER_RELATIVE] 83 | return np.expand_dims(obs, axis=2) 84 | 85 | def process_state_batch(self, batch): 86 | """Processes an entire batch of states and returns it""" 87 | batch = np.swapaxes(batch, 0, 1) 88 | return batch[0] 89 | 90 | # Define the environment 91 | 92 | class Environment(sc2_env.SC2Env): 93 | """Starcraft II environmet. Implementation details in lib/features.py""" 94 | 95 | def __init__(self): 96 | # Creates common pysc2 objects required for running of agent. 97 | agent_interface_format=features.AgentInterfaceFormat( 98 | feature_dimensions=features.Dimensions(screen=FLAGS.screen_size, minimap=FLAGS.minimap_size)) 99 | feats = features.Features(agent_interface_format) 100 | action_spec = feats.action_spec() 101 | super().__init__(map_name=FLAGS.mini_game, 102 | visualize=FLAGS.visualize, 103 | game_steps_per_episode=FLAGS.steps_per_ep, 104 | agent_interface_format=agent_interface_format) 105 | # Save variables which can help in debugging or better unds-ing pysc2: 106 | self.observation_spec = self.observation_spec() 107 | self.agent_interface_format = agent_interface_format 108 | self.feats = feats 109 | self.action_spec = action_spec 110 | # Initialize variables: 111 | self.episode_reward = 0 112 | self.observation_cur = None 113 | 114 | def step(self, action): 115 | """Apply actions, step the world forward, and return observations""" 116 | if FLAGS.verbose: 117 | print('{} AVAILABLE ACTIONS.'.format(len(self.observation_cur.available_actions))) 118 | # Uncomment to print individual available actions. 119 | # for a in observation_cur.available_actions: 120 | # print(actions.FUNCTIONS[a]) 121 | 122 | chosen_func_id = np.random.choice(self.observation_cur.available_actions) 123 | 124 | if FLAGS.verbose: 125 | chosen_action = actions.FUNCTIONS[chosen_func_id] 126 | print('Chosen action: ', chosen_func_id, '~~', chosen_action) 127 | 128 | # Uncomment to print "template" args of chosen action: 129 | # allargs = self.action_spec.functions[chosen_func_id].args 130 | # for arg in allargs: 131 | # print(arg); print('arg sizes ', arg.sizes) 132 | 133 | chosen_args = [[np.random.randint(0, size) for size in arg.sizes] for arg in self.action_spec.functions[chosen_func_id].args] 134 | 135 | if FLAGS.verbose: 136 | print('Chosen args: ', chosen_args) 137 | 138 | action = actions.FunctionCall(chosen_func_id, chosen_args) 139 | obs = super(Environment, self).step([action]) 140 | self.observation_cur = obs[0].observation 141 | r = obs[0].reward 142 | done = obs[0].step_type == pysc2environment.StepType.LAST 143 | self.episode_reward += r 144 | time.sleep(FLAGS.pause) 145 | return obs, r, done, {} 146 | 147 | def reset(self): 148 | obs = super(Environment, self).reset() 149 | self.observation_cur = obs[0].observation 150 | self.episode_reward = 0 151 | return obs 152 | 153 | def actions_to_choose(): 154 | hall = [_HAL_ADEPT, _HAL_ARCHON, _HAL_COL, _HAL_DISRUP, 155 | _HAL_HIGTEM, _HAL_IMN, _HAL_PHOENIX, _HAL_STALKER, 156 | _HAL_VOIDRAID, _HAL_ZEALOT, _FORCE_FIELD, _GUARD_FIELD] 157 | action = actions.FunctionCall(_HAL_ADEPT, [_NOT_QUEUED]) 158 | print(action) 159 | return action 160 | 161 | # TO-DO : Define actions_to_choose based on SC2 sentry unit 162 | 163 | # Agent architecture using keras rl 164 | 165 | def neural_network_model(input, actions): 166 | model = Sequential() 167 | # Define CNN model 168 | print(input) 169 | model.add(Conv2D(256, kernel_size=(5, 5), input_shape=input)) 170 | model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None)) 171 | model.add(Flatten()) 172 | 173 | model.add(Dense(256, activation='relu')) 174 | model.add(Reshape((1, 256))) 175 | 176 | model.add(LSTM(256)) 177 | model.add(Dense(actions, activation='softmax')) 178 | model.summary() 179 | model.compile(loss="categorical_crossentropy", 180 | optimizer="adam", 181 | metrics=["accuracy"]) 182 | 183 | return model 184 | 185 | 186 | def training_game(): 187 | env = Environment() 188 | 189 | input_shape = (FLAGS.screen_size, FLAGS.screen_size, 1) 190 | nb_actions = 12 # Number of actions 191 | 192 | model = neural_network_model(input_shape, nb_actions) 193 | memory = SequentialMemory(limit=5000, window_length=_WINDOW_LENGTH) 194 | 195 | processor = SC2Proc() 196 | 197 | # Policy 198 | 199 | policy = LinearAnnealedPolicy(EpsGreedyQPolicy(), attr="eps", value_max=1, value_min=0.7, value_test=.0, nb_steps=1e6) 200 | 201 | # Agent 202 | 203 | dqn = DQNAgent(model=model, 204 | nb_actions=nb_actions, 205 | memory=memory, 206 | enable_double_dqn=False, 207 | nb_steps_warmup=500, 208 | # nb_steps_warmup=1, 209 | target_model_update=1e-2, 210 | policy=policy, 211 | batch_size=150, 212 | processor=processor) 213 | 214 | dqn.compile(Adam(lr=.001), metrics=["mae"]) 215 | 216 | # Tensorboard callback 217 | 218 | callbacks = keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0, 219 | write_graph=True, write_images=False) 220 | 221 | 222 | # Save the parameters and upload them when needed 223 | 224 | name = FLAGS.mini_game 225 | w_file = "dqn_{}_weights.h5f".format(name) 226 | check_w_file = "train_w" + name + "_weights.h5f" 227 | 228 | if SAVE_MODEL: 229 | check_w_file = "train_w" + name + "_weights_{step}.h5f" 230 | 231 | log_file = "training_w_{}_log.json".format(name) 232 | 233 | if LOAD_MODEL: 234 | dqn.load_weights(w_file) 235 | 236 | dqn.fit(env, callbacks=callbacks, nb_steps=1e7, action_repetition=2, 237 | log_interval=1e4, verbose=2) 238 | 239 | dqn.save_weights(w_file, overwrite=True) 240 | dqn.test(env, action_repetition=2, nb_episodes=30, visualize=False) 241 | 242 | 243 | if __name__ == '__main__': 244 | print('FLAGS:') 245 | for k in FLAGS._flags(): 246 | print(k, FLAGS[k].value) 247 | print('-'*20) 248 | training_game() 249 | -------------------------------------------------------------------------------- /Agents/README.md: -------------------------------------------------------------------------------- 1 | # Sentry Agent mini-game Map exploration 2 | 3 | This part of the repository aims to post several agents regarding functions of sentry unit. 4 | SentryDefense.py --contains all the actions for sentrydefense unit 5 | scripted_agent.py --contains tests for Forcefield Starcraft 2 map 6 | 7 | ### Sentry unit scripted bot running 8 | 9 | --Clone the repo 10 | 11 | --Put ForceField.sc2 map into your minigames map folder 12 | 13 | --Go to pysc2/maps/mini_games.py and add ForceField map to the array map 14 | 15 | --In the /pysc2/agents/ folder type 16 | 17 | ``` 18 | $ python3 -m pysc2.bin.agent --agent scripted_agent.SentryForceField --map ForceField 19 | ``` 20 | ### About the agents 21 | 22 | -- scripted_gent.py --- > scripted -Tested- 23 | 24 | -- q_learning_agent.py --- > learning agent - Tested - 25 | 26 | -- DQN_Agent.py --- > learning agent - Tested - 27 | 28 | -- DQN_Agent_LSTM.py --- > learning agent - Tested - Architecture bellow 29 | 30 | After executing file, type in console : 31 | 32 | ``` 33 | $ tensorboard --logdir path/Graph --host localhost --port 8088 34 | ``` 35 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/Captura%20de%20pantalla%202018-08-13%20a%20las%2015.43.15.png) 36 | 37 | 38 | ### Debugging and testing 39 | 40 | 41 | #### Print available actions 42 | 43 | Will print the id of the available actions in a list 44 | 45 | action_no = actions.FunctionCall(_NO_OP, []) 46 | obs_no = super(Environment, self).step([action_no]) 47 | actions_available = obs_no[0].observation.available_actions 48 | print(actions_available) 49 | -------------------------------------------------------------------------------- /Agents/q_learning_agent.py: -------------------------------------------------------------------------------- 1 | # Q-learning agent for HallucinIce. 2 | # The goal for the agent is to discover wich combination of Hallucination will be better to defeat Terran 3 | # Kudos to Steven Brown and to MorvanZhou 4 | 5 | import random 6 | import math 7 | 8 | import numpy as np 9 | import pandas as pd 10 | 11 | from pysc2.agents import base_agent 12 | from pysc2.lib import actions 13 | from pysc2.lib import features 14 | 15 | # Features 16 | _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index 17 | _PLAYER_FRIENDLY = 1 18 | _PLAYER_HOSTILE = 4 19 | _UNIT_TYPE = features.SCREEN_FEATURES.unit_type.index 20 | _PLAYER_ID = features.SCREEN_FEATURES.player_id.index 21 | _PLAYER_SELF = 1 22 | 23 | # Functions 24 | _NO_OP = actions.FUNCTIONS.no_op.id 25 | _SELECT_POINT = actions.FUNCTIONS.select_point.id 26 | _MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id 27 | _ATTACK_SCREEN = actions.FUNCTIONS.Attack_screen.id 28 | _SELECT_ARMY = actions.FUNCTIONS.select_army.id # Attack_minimap.id 29 | 30 | # Parameters 31 | _NOT_QUEUED = [0] 32 | _SELECT_ALL = [0] 33 | 34 | # Unit IDs 35 | PROTOSS_SENTRY = 77 36 | TERRAN_HELLION = 53 37 | 38 | # Define the actions 39 | ACTION_DO_NOTHING = 'donothing' 40 | ACTION_SELECT_SENTRY = 'selectsentry' 41 | ACTION_HAL_ADEPT = 'adept' 42 | ACTION_HAL_ARCHON = 'archon' 43 | ACTION_HAL_COL = 'colosus' 44 | ACTION_HAL_DISRUP = 'disruptor' 45 | ACTION_HAL_HIGTEM = 'higtem' 46 | ACTION_HAL_IMN = 'imortal' 47 | ACTION_HAL_PHOENIX = 'phoenix' 48 | ACTION_HAL_STALKER = 'stalker' 49 | ACTION_HAL_VOIDRAID = 'voidraid' 50 | ACTION_HAL_ZEALOT = 'zealot' 51 | ACTION_ATTACK = 'attack' 52 | 53 | # Define the call to the API of the Hallucination function 54 | _HAL_ADEPT = actions.FUNCTIONS.Hallucination_Adept_quick.id 55 | _HAL_ARCHON = actions.FUNCTIONS.Hallucination_Archon_quick.id 56 | _HAL_COL = actions.FUNCTIONS.Hallucination_Colossus_quick.id 57 | _HAL_DISRUP = actions.FUNCTIONS.Hallucination_Disruptor_quick.id 58 | _HAL_HIGTEM = actions.FUNCTIONS.Hallucination_HighTemplar_quick.id 59 | _HAL_IMN = actions.FUNCTIONS.Hallucination_Immortal_quick.id 60 | _HAL_PHOENIX = actions.FUNCTIONS.Hallucination_Phoenix_quick.id 61 | _HAL_STALKER = actions.FUNCTIONS.Hallucination_Stalker_quick.id 62 | _HAL_VOIDRAID = actions.FUNCTIONS.Hallucination_VoidRay_quick.id 63 | _HAL_ZEALOT = actions.FUNCTIONS.Hallucination_Zealot_quick.id 64 | _FORCE_FIELD = actions.FUNCTIONS.Effect_ForceField_screen.id 65 | _GUARD_FIELD = actions.FUNCTIONS.Effect_GuardianShield_quick.id 66 | 67 | 68 | print(_HAL_ARCHON) 69 | 70 | smart_actions = [ 71 | ACTION_DO_NOTHING, 72 | ACTION_HAL_ADEPT, 73 | ACTION_HAL_ARCHON, 74 | ACTION_HAL_COL, 75 | ACTION_HAL_DISRUP, 76 | ACTION_HAL_HIGTEM, 77 | ACTION_HAL_IMN, 78 | ACTION_HAL_PHOENIX, 79 | ACTION_HAL_STALKER, 80 | ACTION_HAL_VOIDRAID, 81 | ACTION_HAL_ZEALOT, 82 | ACTION_ATTACK, 83 | ] 84 | 85 | KILL_UNIT_REWARD = 0.5 86 | 87 | 88 | class QLearningTable: 89 | def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9): 90 | self.actions = actions # a list? 91 | self.lr = learning_rate 92 | self.gamma = reward_decay 93 | self.epsilon = e_greedy 94 | self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64) 95 | 96 | def choose_action(self, observation): 97 | self.check_state_exist(observation) 98 | 99 | if np.random.uniform() < self.epsilon: 100 | # choose the best action from q-table 101 | state_action = self.q_table.ix[observation, :] 102 | 103 | # some actions have the same value 104 | state_action = state_action.reindex(np.random.permutation(state_action.index)) 105 | 106 | action = state_action.idxmax() 107 | else: 108 | # choose random action 109 | action = np.random.choice(self.actions) 110 | 111 | return action 112 | 113 | # Q-learning implementation 114 | 115 | def learn(self, s, a, r, s_): 116 | self.check_state_exist(s_) 117 | self.check_state_exist(s) 118 | 119 | # Make q-table and select max value 120 | 121 | q_predict = self.q_table.ix[s, a] 122 | q_target = r + self.gamma * self.q_table.ix[s_, :].max() 123 | 124 | # update 125 | 126 | self.q_table.ix[s, a] += self.lr * (q_target - q_predict) 127 | 128 | def check_state_exist(self, state): 129 | if state not in self.q_table.index: 130 | self.q_table = self.q_table.append( 131 | pd.Series([0] * len(self.actions), index=self.q_table.columns, name=state)) 132 | 133 | 134 | class SmartAgent(base_agent.BaseAgent): 135 | def __init__(self): 136 | super(SmartAgent, self).__init__() # For fix trouble with undefined properties 137 | self.qlearn = QLearningTable(actions=list(range(len(smart_actions)))) 138 | 139 | self.previous_killed_unit_score = 0 140 | self.previous_action = None 141 | self.previous_state = None 142 | 143 | # Compare step from scripted agent with learning agent 144 | 145 | def step(self, obs): 146 | super(SmartAgent, self).step(obs) 147 | 148 | def _xy_locs(mask): 149 | """Mask should be a set of bools from comparison with a feature layer""" 150 | y, x = mask.nonzero() 151 | return list(zip(x, y)) 152 | 153 | PLAYER_RELATIVE = obs.observation.feature_screen.player_relative 154 | player_ = _xy_locs(PLAYER_RELATIVE == _PLAYER_SELF) 155 | if not player_: 156 | return _NO_OP 157 | # player_center = numpy.mean(player_, axis=0).round() 158 | else: 159 | return actions.FUNCTIONS.select_army("select") 160 | 161 | 162 | ##unit_type = obs.observation['screen'][_UNIT_TYPE] is this here or inside fork ? 163 | 164 | sentry_y, sentry_x = (unit_type == PROTOSS_SENTRY).nonzero() 165 | n_sentry_count = 1 if sentry_y.any() else 0 166 | 167 | enemy_y, enemy_x = (unit_type == TERRAN_HELLION).nonzero() 168 | n_enemies_count = 1 if enemy_y.any() else 0 169 | 170 | current_state = [ 171 | n_sentry_count, 172 | hallucinations_count, # how does this method work? 173 | n_enemies_count, 174 | army_supply, # this comma exists? 175 | ] 176 | 177 | if self.previous_action is not None: 178 | reward = 0 179 | 180 | if killed_unit_score > self.previous_killed_score: 181 | reward += KILL_UNIT_REWARD 182 | 183 | self.qlearn.learn(str(self.previous_state), self.previous_action, reward, str(current_state)) 184 | 185 | rl_action = self.qlearn.choose_action(str(current_state)) 186 | smart_action = Smart_actions[rl_action] 187 | 188 | self.previous_killed_unit_score = killed_unit_score 189 | self.previous_state = current_state 190 | self.previous_action = rl_action 191 | 192 | if smart_action == ACTION_DO_NOTHING: 193 | return actions.FunctionCall(_NO_OP, []) 194 | 195 | elif smart_action == ACTION_SELECT_SENTRY: 196 | unit_type = obs.observation['screen'][_UNIT_TYPE] 197 | unit_y, unit_x = (unit_type == _SENTRY).nonzero() 198 | 199 | if unit_y.any(): 200 | i = random.randint(o, len(unit_y) - 1) 201 | target = [unit_x[i], unit_y[i]] 202 | 203 | return actions.FunctionCall(_SELECT_POINT, [_NOT_QUEUED, target]) 204 | 205 | elif smart_action == ACTION_HAL_ARCHON: 206 | if _HAL_ARCHON in obs.observation["available_actions"]: 207 | return actions.FunctionCall(_HAL_ARCHON, [_NOT_QUEUED]) 208 | 209 | elif smart_action == ACTION_HAL_ADEPT: 210 | if _HAL_ADEPT in obs.observation["available_actions"]: 211 | return actions.FunctionCall(_HAL_ADEPT, [_NOT_QUEUED]) 212 | 213 | elif smart_action == ACTION_HAL_COL: 214 | if _HAL_COL in obs.observation["available_actions"]: 215 | return actions.FunctionCall(_HAL_ADEPT, [_NOT_QUEUED]) 216 | 217 | elif smart_action == ACTION_HAL_DISRUP: 218 | if _HAL_DISRUP in obs.observation["available_actions"]: 219 | return actions.FunctionCall(_HAL_ADEPT, [_NOT_QUEUED]) 220 | 221 | elif smart_action == ACTION_HIGTEM: 222 | if _HAL_HIGTEM in obs.observation["available_actions"]: 223 | return actions.FunctionCall(_HAL_ADEPT, [_NOT_QUEUED]) 224 | 225 | elif smart_action == ACTION_PHOENIX: 226 | if _HAL_PHOENIX in obs.observation["available_actions"]: 227 | return actions.FunctionCall(_HAL_ADEPT, [_NOT_QUEUED]) 228 | 229 | elif smart_action == ACTION_STALKER: 230 | if _HAL_STALKER in obs.observation["available_actions"]: 231 | return actions.FunctionCall(_HAL_ADEPT, [_NOT_QUEUED]) 232 | 233 | elif smart_action == ACTION_ATTACK: 234 | if _ATTACK_MINIMAP in obs.obsservation["available_actions"]: 235 | if self.base_top_left: 236 | return actions.FunctionCall(_ATTACK_MINIMAP, [_NOT_QUEUED], [77, 53]) 237 | 238 | return actions.FunctionCall(_ATTACK_MINIMAP, [_NOT_QUEUED], [77, 53]) 239 | 240 | return actions.FunctionCall(_NO_OP, []) 241 | -------------------------------------------------------------------------------- /Agents/scripted_agent.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 Google Inc. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS-IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # Special thanks to : jmathison 15 | # thebunny 16 | # AleKahpwn 17 | 18 | from __future__ import absolute_import 19 | from __future__ import division 20 | from __future__ import print_function 21 | 22 | import numpy 23 | import random 24 | 25 | from pysc2.agents import base_agent 26 | from pysc2.lib import actions 27 | from pysc2.lib import features 28 | 29 | 30 | FUNCTIONS = actions.FUNCTIONS 31 | _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index 32 | _PLAYER_FRIENDLY = 1 33 | _PLAYER_NEUTRAL = 3 # beacon/minerals 34 | _PLAYER_HOSTILE = 4 35 | _NO_OP = FUNCTIONS.no_op.id 36 | _MOVE_SCREEN = FUNCTIONS.Move_screen.id 37 | _ATTACK_SCREEN = FUNCTIONS.Attack_screen.id 38 | _SELECT_ARMY = FUNCTIONS.select_army.id 39 | _NOT_QUEUED = [0] 40 | _SELECT_ALL = [0] 41 | _HAL_ADEPT = FUNCTIONS.Hallucination_Adept_quick.id 42 | _HAL_ARCHON = FUNCTIONS.Hallucination_Archon_quick.id 43 | _HAL_COL = FUNCTIONS.Hallucination_Colossus_quick.id 44 | _HAL_DISRUP = FUNCTIONS.Hallucination_Disruptor_quick.id 45 | _HAL_HIGTEM = FUNCTIONS.Hallucination_HighTemplar_quick.id 46 | _HAL_IMN = FUNCTIONS.Hallucination_Immortal_quick.id 47 | _HAL_PHOENIX = FUNCTIONS.Hallucination_Phoenix_quick.id 48 | _HAL_STALKER = FUNCTIONS.Hallucination_Stalker_quick.id 49 | _HAL_VOIDRAID = FUNCTIONS.Hallucination_VoidRay_quick.id 50 | _HAL_ZEALOT = FUNCTIONS.Hallucination_Zealot_quick.id 51 | _FORCE_FIELD = FUNCTIONS.Effect_ForceField_screen.id 52 | _GUARD_FIELD = FUNCTIONS.Effect_GuardianShield_quick.id 53 | 54 | class SentryForceField(base_agent.BaseAgent): 55 | """An agent specifically for solving the ForceField map.""" 56 | 57 | def step(self, obs): 58 | super(SentryForceField, self).step(obs) 59 | if _FORCE_FIELD in obs.observation["available_actions"]: 60 | player_relative = obs.observation.feature_screen.player_relative 61 | hydralisk_y, hydralisk_x = (player_relative == _PLAYER_HOSTILE).nonzero() 62 | if not hydralisk_y.any(): 63 | return FUNCTIONS.no_op() 64 | index = numpy.argmax(hydralisk_y) 65 | target = [hydralisk_x[index], hydralisk_y[index]] 66 | return FUNCTIONS.Effect_ForceField_screen("now", target) 67 | elif _SELECT_ARMY in obs.observation["available_actions"]: 68 | return FUNCTIONS.select_army("select") 69 | else: 70 | return FUNCTIONS.no_op() 71 | 72 | Hallucinations_list = [_HAL_ADEPT, _HAL_ARCHON, _HAL_DISRUP, _HAL_HIGTEM, _HAL_IMN, _HAL_PHOENIX, _HAL_STALKER, _HAL_VOIDRAID, _HAL_ZEALOT] 73 | 74 | 75 | 76 | class HallucinationArchon(base_agent.BaseAgent): 77 | """An agent specifically for solving the HallucinIce map with Archon Unit.""" 78 | 79 | def step(self, obs): 80 | super(HallucinationArchon, self).step(obs) 81 | if _HAL_ARCHON in obs.observation["available_actions"]: 82 | player_relative = obs.observation["feature_screen"][_PLAYER_RELATIVE] 83 | hellion_y, hellion_x = (player_relative == _PLAYER_HOSTILE).nonzero() 84 | if not hellion_y.any(): 85 | return actions.FunctionCall(_NO_OP, []) 86 | index = numpy.argmax(hellion_y) 87 | target = [hellion_x[index], hellion_y[index]] 88 | return actions.FunctionCall(_HAL_ARCHON, [_NOT_QUEUED]) 89 | elif _SELECT_ARMY in obs.observation["available_actions"]: 90 | return actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL]) 91 | else: 92 | return actions.FunctionCall(_NO_OP, []) 93 | 94 | 95 | Hallucinations = [_HAL_ADEPT, _HAL_ARCHON, _HAL_DISRUP, _HAL_HIGTEM, _HAL_IMN, _HAL_PHOENIX, _HAL_STALKER, _HAL_VOIDRAID, _HAL_ZEALOT] 96 | 97 | class Hallucination(base_agent.BaseAgent): 98 | """An agent specifically for solving the HallucinIce map with Random Hallucination.""" 99 | 100 | def step(self, obs): 101 | test = random.randrange(0, len(Hallucinations_list) - 1) 102 | super(Hallucination, self).step(obs) 103 | 104 | score_general = obs.observation["score_cumulative"][0] 105 | value_units = obs.observation["score_cumulative"][3] 106 | kill_value_units = obs.observation["score_cumulative"][5] 107 | 108 | print("score general is ", score_general) 109 | print("value units is ", value_units) 110 | print("kill value units is", kill_value_units) 111 | 112 | if Hallucinations_list[test] in obs.observation["available_actions"]: 113 | player_relative = obs.observation["feature_screen"][_PLAYER_RELATIVE] 114 | hellion_y, hellion_x = (player_relative == _PLAYER_HOSTILE).nonzero() 115 | if not hellion_y.any(): 116 | return actions.FunctionCall(_NO_OP, []) 117 | index = numpy.argmax(hellion_y) 118 | target = [hellion_x[index], hellion_y[index]] 119 | print(random.randrange(0, len(Hallucinations_list))) 120 | 121 | return actions.FunctionCall(Hallucinations_list[test], [_NOT_QUEUED]) 122 | elif _SELECT_ARMY in obs.observation["available_actions"]: 123 | return FUNCTIONS.select_army("select") 124 | else: 125 | return FUNCTIONS.no_op() 126 | 127 | 128 | class StalkerControl(base_agent.BaseAgent): 129 | """An agent specifically for solving the DefeatZealotsMap map without Blink but with micro """ 130 | 131 | base_top_left = None 132 | 133 | def transformLocation(self, x, x_distance, y, y_distance): 134 | if not self.base_top_left: 135 | return [x - x_distance, y - y_distance]= 136 | return [x + x_distance, y + y_distance] 137 | 138 | 139 | def step(self, obs): 140 | super(StalkerControl, self).step(obs) 141 | if _______ in obs.observation["available_actions"]: 142 | player_relative = obs.observation["feature_screen"][_PLAYER_RELATIVE] 143 | zealot_y, zealot_x = (player_relative == _PLAYER_HOSTILE).nonzero() 144 | if not zealot_y.any(): 145 | return actions.FunctionCall(_NO_OP, []) 146 | index = numpy.argmax(zealot_y) 147 | target = [zealot_x[index], zealot_y[index]] 148 | return actions.FunctionCall(_________, [_NOT_QUEUED]) 149 | elif _SELECT_ARMY in obs.observation["available_actions"]: 150 | return actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL]) 151 | else: 152 | return actions.FunctionCall(_NO_OP, []) 153 | -------------------------------------------------------------------------------- /Agents/sentry_defense.py: -------------------------------------------------------------------------------- 1 | 2 | from __future__ import absolute_import 3 | from __future__ import division 4 | from __future__ import print_function 5 | 6 | import numpy 7 | from pysc2.agents import base_agent 8 | from pysc2.lib import features 9 | from pysc2.lib import actions 10 | 11 | _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index 12 | _PLAYER_FRIENDLY = 1 13 | _PLAYER_HOSTILE = 4 14 | _NO_OP = actions.FUNCTIONS.no_op.id 15 | _MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id 16 | _ATTACK_SCREEN = actions.FUNCTIONS.Attack_screen.id 17 | _SELECT_ARMY = actions.FUNCTIONS.select_army.id 18 | _NOT_QUEUED = [0] 19 | _SELECT_ALL = [0] 20 | _FORCE_FIELD = actions.FUNCTIONS.Effect_ForceField_screen.id 21 | 22 | class SentryForceField(base_agent.BaseAgent): 23 | """An agent specifically for solving the ForceField map.""" 24 | 25 | def step(self, obs): 26 | super(SentryForceField, self).step(obs) 27 | if _FORCE_FIELD in obs.observation["available_actions"]: 28 | player_relative = obs.observation["screen"][_PLAYER_RELATIVE] 29 | hydralisk_y, hydralisk_x = (player_relative == _PLAYER_HOSTILE).nonzero() 30 | if not hydralisk_y.any(): 31 | return actions.FunctionCall(_NO_OP, []) 32 | index = numpy.argmax(hydralisk_y) 33 | target = [hydralisk_x[index], hydralisk_y[index]] 34 | return actions.FunctionCall(_FORCE_FIELD, [_NOT_QUEUED, target]) 35 | elif _SELECT_ARMY in obs.observation["available_actions"]: 36 | return actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL]) 37 | else: 38 | return actions.FunctionCall(_NO_OP, []) 39 | 40 | 41 | 42 | class Sentry(): 43 | '''Defines how the sentry SC2 unit works''' 44 | 45 | 46 | def Force_Field(sentry): 47 | '''Function related with Force Field creation''' 48 | _FORCE_FIELD = actions.FUNCTIONS.Effect_ForceField_screen.id 49 | 50 | 51 | def Guardian_Shield(sentry): 52 | '''Function related with Shield creation''' 53 | _GUARD_FIELD = actions.FUNCTIONS.Effect_GuardianShield_quick.id 54 | 55 | def Hallucinations(sentry): 56 | '''Functions related with Hallucination''' 57 | 58 | _HAL_ADEPT = actions.FUNCTIONS.Hallucination_Adept_quick.id 59 | _HAL_ARCHON = actions.FUNCTIONS.Hallucination_Archon_quick.id 60 | _HAL_COL = actions.FUNCTIONS.Hallucination_Colossus_quick.id 61 | _HAL_DISRUP = actions.FUNCTIONS.Hallucination_Disruptor_quick.id 62 | _HAL_HIGTEM = actions.FUNCTIONS.Hallucination_HighTemplar_quick.id 63 | _HAL_IMN = actions.FUNCTIONS.Hallucination_Immortal_quick.id 64 | _HAL_PHOENIX = actions.FUNCTIONS.Hallucination_Phoenix_quick.id 65 | _HAL_STALKER = actions.FUNCTIONS.Hallucination_Stalker_quick.id 66 | _HAL_VOIDRAID = actions.FUNCTIONS.Hallucination_VoidRay_quick.id 67 | _HAL_ZEALOT = actions.FUNCTIONS.Hallucination_Zealot_quick.id 68 | return actions.FunctionCall(actions.FUNCTIONS.no_op.id, []) 69 | 70 | def Standard_Functions(sentry): 71 | '''Standard Functions related with movements and exploration ''' 72 | _NOOP = actions.FUNCTIONS.no_op.id 73 | _SELECT_POINT = actions.FUNCTIONS.select_point.id 74 | return actions.FunctionCall(actions.FUNCTIONS.no_op.id, []) 75 | 76 | 77 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | 2 | This is an open contributing project 3 | Significant accepted pull request are given authory to any contributions following Apache license and will be noted and named 4 | in any given talk or project citation. 5 | 6 | This project is searching for : 7 | 8 | * Starcraft2 hardcore players and map creators for interviews, creation and feedback. 9 | 10 | * Software developers that can help within agent programming 11 | -------------------------------------------------------------------------------- /ISSUE_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | Issue Template description 2 | =========== 3 | 4 | Context 5 | ------------ 6 | 7 | Describe the context you are working on, it can include : 8 | * op sistem / machine 9 | * level of experience with this library 10 | * Starcraft 2 map designer or agent developer experience 11 | 12 | Problem 13 | ------------ 14 | 15 | Describe the problem you finded with this repo, as this might include : 16 | * what is the goal you want to achieve 17 | * if you didn´t find suitable documentation 18 | 19 | Issue resolution 20 | ------------ 21 | 22 | Please note that you might be asked for some questions in order to solve the issue 23 | 24 | -------------------------------------------------------------------------------- /Images/2C01EB1027814BB7FF16A15272E1B2DEF9FDEEC3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/Images/2C01EB1027814BB7FF16A15272E1B2DEF9FDEEC3.jpg -------------------------------------------------------------------------------- /Images/Captura de pantalla 2017-09-03 a las 12.05.18.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/Images/Captura de pantalla 2017-09-03 a las 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General purpose learning system for Startcraft 2 can be a daunting task. So there is a logical option in splitting this tasks into minitask in orther to advance in research . Mini-games focus on different elements of Starcraft II Gameplay . 17 | 18 | To investigate elements of the game in isolation, and to provide further fine-grained steps towards playing the full game, Deepmind has built several mini-games. These are focused scenarios on small maps that have been constructed with the purpose of testing a subset of actions and/or game mechanics with a clear reward structure. Unlike the full game where the reward is just win/lose/tie, the reward structure for mini-games can reward particular behaviours (as defined in a corresponding .SC2Map file). 19 | 20 | ## Minigame introduction and Repo information 21 | Before creating a minigame, I encourage you to run the alredy developed ones to see wich task are subdivided into each minigame as the reward function could be important due to the behaviour that leads to learning . The minigame title gives us a description of the goal 22 | You might find in this repo new maps created for investigate in explotation-exploration dilema and some programming about the functions of different units . 23 | 24 | ## SentryDefense , ForceField , HallucinIce and FlowerFields Projects 25 | 26 | In this repo, besides information about current minigames, you will find my own minigames development . 27 | In projects section you can know more about the state of the art and in docs and new_minigames folder you can find the map. 28 | In SentryDefense, a arrowhead TerranVSProtoss Melee is proposed . 29 | In ForceField,an imbalanced situation between Sentry and Zerg units forces sentry to use forcefield, adding terrain disposition. 30 | 31 | 1.- [SentryDefense](https://github.com/SoyGema/Startcraft_pysc2_minigames/tree/master/new_minigames/SentryDefense): Protoss VS Terran Melee. 32 | 33 | 2.- [ForceField](https://github.com/SoyGema/Startcraft_pysc2_minigames/tree/master/new_minigames/SentryForceField): Learn about Sentry forcefield function. 34 | 35 | 3.- [HallucinIce](https://github.com/SoyGema/Startcraft_pysc2_minigames/tree/master/new_minigames/SentryHallucination): Learn how to play with hallucination. 36 | 37 | 4.- [FlowerFields](https://github.com/SoyGema/Startcraft_pysc2_minigames/tree/master/new_minigames/FlowerFields): Defeat protoss photon cannon . 38 | 39 | 5.-[TheRightPath](https://github.com/SoyGema/Startcraft_pysc2_minigames/tree/master/new_minigames/TheRightPath): Move to beacon finding the optimal route collecting minerals and avoiding mines . 40 | 41 | 6.-[RedWaves](https://github.com/SoyGema/Startcraft_pysc2_minigames/tree/master/new_minigames/RedWaves): Choose your race and defend against waves of zerg attacks . 42 | 43 | 7.-[BlueMoon](https://github.com/SoyGema/Startcraft_pysc2_minigames/tree/master/new_minigames/BlueMoon): Choose unit development to defend against protoss development . (under construction) 44 | 45 | 8.-[MicroPrism](https://github.com/SoyGema/Startcraft_pysc2_minigames/tree/master/new_minigames/MicroPrism): Learn how to use Warp Prism in a protoss versus protoss stalker melee 46 | 47 | ## Agents 48 | Regarding scripted agent, there is a python file with several developments. Scriptedagent.py is focused on HallucinIce map in which makes Archon Hallucination. Besides there is another class that put all hallucination actions on a list and the agent chooses randomly in between those actions . 49 | 50 | Q-Learning and DQN agents are provided for HallucinIce minigame with the new PySC2 release 51 | 52 | An A3C trained agent has been tested with several minigames, reaching some of them a local optima . 53 | Please, report problems in issues if you currently find problems . 54 | 55 | ## How to run mini-games in your environment 56 | 57 | 1. Download or clone the repository, or download all minigames clicking here 58 | 59 | 2. Place the .SC2 files into /Applications/StarCraft II/Maps/mini_games/ -sometimes the Map folder might not exist. If so, please create it- 60 | 61 | 3. Go to pysc2\maps\mini_games.py and add to mini-games array the following mini-games names 62 | 63 | ```python 64 | mini_games = [ ## This mini-games names should alredy been in your list 65 | "BuildMarines", # 900s 66 | "CollectMineralsAndGas", # 420s 67 | "CollectMineralShards", # 120s 68 | "DefeatRoaches", # 120s 69 | "DefeatZerglingsAndBanelings", # 120s 70 | "FindAndDefeatZerglings", # 180s 71 | "MoveToBeacon", # 120s ##Now you add this few lines 72 | "SentryDefense", # 120s 73 | "ForceField", # 30s 74 | "HallucinIce", # 30s 75 | "FlowerFields", # 60s 76 | "TheRightPath", # 300s 77 | "RedWaves", # 180s 78 | "BlueMoon", # 60s 79 | "MicroPrism", # 45s 80 | ] 81 | ``` 82 | 4. In your console, you can type the mini-game map name 83 | 84 | ```console 85 | my-computer:~ me$ python -m pysc2.bin.agent --map FlowerFields 86 | ``` 87 | 88 | 89 | #### Tutorial 90 | Find an ongoing tutorial about how to create your own mini-game [here](https://github.com/SoyGema/Startcraft_pysc2_minigames/tree/master/docs) 91 | 92 | #### Other repository 93 | With features with map placement [here](https://github.com/ttinies/sc2gameMapRepo) 94 | 95 | 96 | 97 | 'Making a reward function isn’t that difficult. The difficulty comes when you try to design a reward function that encourages the behaviors you want while still being learnable.' 98 | -Deep Reinforcement Learning Doesn´t work yet , Alex Irpan [post](https://www.alexirpan.com/2018/02/14/rl-hard.html)- 99 | -------------------------------------------------------------------------------- /RL/README.md: -------------------------------------------------------------------------------- 1 | Notes and references about reinforcement learning for pySC2 2 | 3 | -------------------------------------------------------------------------------- /Replays/DefeatRoaches_2017-09-08-13-59-49.SC2Replay: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/Replays/DefeatRoaches_2017-09-08-13-59-49.SC2Replay -------------------------------------------------------------------------------- /Replays/README.md: -------------------------------------------------------------------------------- 1 | Replays for DefeatRoaches 2 | -------------------------------------------------------------------------------- /SentryDefense.SC2Map: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/SentryDefense.SC2Map -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman -------------------------------------------------------------------------------- /docs/README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | # Startcraft Pysc2 Deepmind mini games creation 4 | This file aims to serve as a guide for opensource contributing in minigame pysc2 library for Artificial Intelligence reserach. In this document we will execute and change the map 'DefeatRoaches' for a 'DefeatWhatever' or some minor changes you could do in the StarCraft II editor for designing our own mini-battle 5 | 6 | For minigame instalation for execution you should go to the ![official repository](https://github.com/deepmind/pysc2) and install requirements 7 | 8 | ## Mini game task description 9 | Mini games come as a controled environments that might be useful to exploit game features in SC2. General purpose learning system for Startcraft 2 can be a daunting task. So there is a logical option in splitting this tasks into minitask in orther to advance in research . 10 | To investigate elements of the game in isolation, and to provide further fine-grained steps towards playing the full game, Deepmind has built several mini-games. These are focused scenarios on small maps that have been constructed with the purpose of testing a subset of actions and/or game mechanics with a clear reward structure. Unlike the full game where the reward is just win/lose/tie, the reward structure for mini-games can reward particular behaviours (as defined in a corresponding .SC2Map file). 11 | 12 | ## Mini game introduction 13 | Before creating a minigame, I encourage you to run the alredy developed ones to see wich task are subdivided into each minigame as the design could be important . The minigame title gives us a description of the goal 14 | Find bellow the exploration of DefeatRoaches mini-game map 15 | 16 | ## From DefeatRoaches to Defeat'Whatever' 17 | 18 | #### Description 19 | 20 | A map with 9 Marines and a group of 4 Roaches on opposite sides. Rewards are earned by using the Marines to defeat Roaches, with optimal combat strategy requiring the Marines to perform focus fire on the Roaches. Whenever all 4 Roaches have been defeated, a new group of 4 Roaches is spawned and the player is awarded 5 additional Marines at full health, with all other surviving Marines retaining their existing health (no restore). Whenever new units are spawned, all unit positions are reset to opposite sides of the map. 21 | 22 | #### Initial State ---------> Transformation UNIT CHANGE 23 | 24 | * 9 Marines in a vertical line at a random side of the map (preselected) -------> 9 sentry 25 | * 4 Roaches in a vertical line at the opposite side of the map from the -------> 4 zerlings 26 | Marines 27 | 28 | #### Rewards --------------> Transformation REWARD SHAPPING 29 | 30 | * Roach defeated: +10 ---------> Zerlings defeated : +5 31 | * Marine defeated: -1 ---------> Sentry defeated : -5 32 | 33 | #### End Conditions 34 | 35 | * Time elapsed 36 | * All Marines defeated ---------> Sentry 37 | 38 | #### Time Limit. ------------> Transformation TIME LIMIT 39 | 40 | * 120 seconds 41 | 42 | ![alt tag](https://github.com/SoyGema/Startcraft/blob/master/Images/Captura%20de%20pantalla%202017-09-03%20a%20las%2012.05.18.png ) 43 | 44 | This is a human interpretable view of the game on the left, and coloured versions of the feature layers on the right. Find in top left described the actions 45 | *a-Attack 46 | *d-Stop 47 | *m-Move 48 | *p-MovePatrol 49 | *t-MoveHoldPosition 50 | Green circles are used to define player1(terran) and red circles correspond to player2(roaches) 51 | This abstraction might be useful for CNN training 52 | 53 | 54 | ## 1. Defeat Roaches Test Map 55 | 56 | Two different type of agents have been executed in this minimap. As a matter of results, you can find bellow listed the difference in between them 57 | 58 | #### Experiment 1 59 | 60 | DefeatRoaches minigame map random agent 61 | 62 | ```shell 63 | $ python3 -m pysc2.bin.agent --map DefeatRoaches 64 | ``` 65 | 66 |
67 | 69 | MarinesVSRoaches Random Agent 72 | 73 | 74 | * Priorization of moving in map (marine player 1) VS attack (roaches player 2) 75 | * Playing for 15min with all defeats for player 1 76 | * Note that this is not a scripted agent. 77 | * The game was executing during 15 minutes approximately 78 | * Find bellow the 17 actions the agent was actually executing during the game 79 | 80 | You can find a complete list of actions ![link](https://github.com/deepmind/pysc2/blob/master/pysc2/lib/actions.py#L553) . 81 | Think that when you are defining your own minimap you should list of actions of units. 82 | 83 | 84 | ![alt tag](https://github.com/SoyGema/Startcraft/blob/master/Images/print_screen_1.png ) 85 | 86 | #### Experiment 2 87 | 88 | DefeatRoaches minigame map scripted agent 89 | 90 | ```shell 91 | $ python3 -m pysc2.bin.agent --map DefeatRoaches --agent pysc2.agents.scripted_agent.DefeatRoaches 92 | ``` 93 | 94 | 96 | MarinesVSRoaches Scripted Agent 99 | 100 | 101 | 102 | * No map random exploration by agent 103 | * Increased number of victories by agent 104 | * However, there seems to be a balance in between . 105 | * In the middle of the game you can see player 1 is currently winning and the agent starts losing as it imbalance by rules games. 106 | * Everytime the game is executed it shows an error at aproximately min 15 107 | * Error has been reported to blizzard team 108 | 109 | 110 | ## 2. Getting into Starcraft 2 Map Editor 111 | 112 | #### Changing units and reward shaping 113 | 114 | Starcraft 2 Map Editor is an extensive piece of software that might be studied independly. 115 | There are currently great tutorials at battlenet in wich you can find detailed information about map creations . You might want to 116 | By now, the most significant concepts that minigame creation need are 117 | 118 | *Terrain. Set the physical environment for your game including playable space and start units 119 | *Triggers . Basic set of instructions and visual programming interface in wich you can set important variables such as : events, conditions and actions 120 | 121 | Inside your Starcraft II folder you should find an executable file for StarCraft II Editor . When you open MarinesVSRoach.SC2Maps you should find something like this 122 | 123 | ##### Terrain 124 | 125 | In this screen you might find a terrain configuration. Playable space and Right / Left areas are settled for different players. In the upper band you might find common open/save copy/paste settings options and unit/terrain edition. 126 | 127 | ![alt tag](https://github.com/SoyGema/Startcraft/blob/master/Images/Captura%20de%20pantalla%202017-09-08%20a%20las%2017.05.07.png) 128 | The playable regions will be related to players in the triggers 129 | The first thing that you might find curious is that there are no units settled down in the terrain . 130 | They are settled in the init visual script 131 | 132 | ##### Triggers 133 | 134 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/trigger.png) 135 | In the top bar you might see an ico 136 | Triggers are divided into List / content full interfaces. 137 | The list shows the variables that you set in your minigame ( in this case a timer, a marine and a roach variable) in wich conditions are created in diferent scripts 138 | ![alt tag](https://github.com/SoyGema/Startcraft/blob/master/Images/Captura%20de%20pantalla%202017-09-08%20a%20las%2017.48.55.png) 139 | 140 | On the left side you can find the trigger list you might need for your map creation. In that list we define some as variables and another ones as visual script that will have a complete function. 141 | Common to all minimaps we have Init trigger, in wich we will setup initial conditions for the minimap . 142 | Top right to trigger list we will find trigger content , in wich we will expand options for init trigger to function. We will find 4 options common to all minimaps 143 | 144 | ###### UNIT CHANGE Changing unit inizialization in Init visual script 145 | 146 | In the init script you might find two ways to create the units : 147 | 148 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/change_units.png) 149 | 150 | ---- A for loop going from 1 to 9 creating Marine unit whith an offset , adding it to a variable created ( Marines ). Renaming this variable into the name of your unit will help you to have . If you change the range from the loop you will have the number of units initialized . 151 | If you double click , in the "Type" section you can select any other unit . In this case I selected sentry 152 | 153 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/change%20Marines.png) 154 | 155 | ----- An inizialization of units added to the group as a variable defined . In this case, several Roaches. 156 | I will change it for Zerlings 157 | 158 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/zerling%20change.png) 159 | 160 | Now, the script should look like this 161 | This means that now the initial setup must be Sentry VS Chalenging (Zerlings) 162 | 163 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/new_init.png) 164 | 165 | ###### REWARD SHAPPING Reward Shaping in Score Updates and Victory script 166 | This script basically makes the victory check about the number of living units that are holded in each variable. 167 | For make the reward shaping , click on "Player -Modify player 1 Curriculum Score: Add 10" and change the value to 5 168 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/Reward%20shaping.png) 169 | 170 | 171 | 172 | Startcraft dataset and AI research 173 | ## Another Useful resources about Startcraft 174 | 175 | Mini games ![list](https://github.com/SoyGema/Startcraft_pysc2_minigames) 176 | 177 | Community ![mini games](https://github.com/SoyGema/pySC2_minigames) 178 | -------------------------------------------------------------------------------- /docs/SentryUnit.md: -------------------------------------------------------------------------------- 1 | 2 | ## Sentry unit information 3 | | Description | Ground unit | 4 | | ------------- | ------------ | 5 | | Built from | Gateway | 6 | | ------------- | ------------ | 7 | | Damage | 6(+1). | 8 | | ------------- | ------------ | 9 | | Speed. | 3.15. | 10 | | ------------- | ------------ | 11 | | Strong | Zealot. | 12 | | against | Zerling. | 13 | | ------------- | ------------ | 14 | | Weak. | Stalker | 15 | | Against | Reaper | 16 | 17 | 18 | ## Sentry abilities info 19 | Find in this table cathegorized special abilities of sentry units 20 | 21 | | Sentry | Enegergy | Duration | Radius | Ability | 22 | | ------------- | -------- | -------- | ------ | -------- | 23 | | Guardian | 50. | 11s. | 4. | Reduce | 24 | | Shield | | | |. harm x2 | 25 | | ------------- | -------- | -------- | ------ | -------- | 26 | | Force | 75. | 11s. |. 1.7 | | 27 | | Field | | |. | | 28 | | ------------- | -------- | -------- | ------ | -------- | 29 | | Hallucination | 75. | 43 |. | fooling | 30 | | ------------- | -------- | -------- | ------ | -------- | 31 | 32 | ## Sentry VS Terran 33 | Sentries can make a huge difference in PvT. 34 | Force Field can be used to block Terrans moving in to try to target key units such as Colossus. 35 | This is vital to ensure that the primary damage dealers of the Protoss army survive. 36 | Another important ability is Guardian Shield. 37 | Since Guardian Shield reduces the damage of each incoming shot by 2, units that attack quickly such as Marines are heavily affected by the ability. 38 | The Sentry can be used to block off the ramp from early rushes. 39 | -------------------------------------------------------------------------------- /docs/Terran/Terran_mini_games.md: -------------------------------------------------------------------------------- 1 | Units and maps about Terran 2 | -------------------------------------------------------------------------------- /docs/_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-hacker -------------------------------------------------------------------------------- /new_minigames/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/.DS_Store -------------------------------------------------------------------------------- /new_minigames/BlueMoon/Images/First_Tech_Core_Protoss.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/BlueMoon/Images/First_Tech_Core_Protoss.png -------------------------------------------------------------------------------- /new_minigames/BlueMoon/Images/Protoss.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/BlueMoon/Images/Protoss.jpg -------------------------------------------------------------------------------- /new_minigames/BlueMoon/Images/README.MD: -------------------------------------------------------------------------------- 1 | Images of Protoss and Terran Tech Tree 2 | -------------------------------------------------------------------------------- /new_minigames/BlueMoon/README.md: -------------------------------------------------------------------------------- 1 | ### Blue Moon 2 | 3 | #### Description 4 | TvP mini-game. 5 | The goal of this mini-game is to explore a scale-up micro and taking into account the tech development of the adversarial race (TechTree) . The objetive of the agent is to maximize the sum of rewards earned through a sequence of adversarial tech development evolution . The good agents will develop tech that responds and is able to beat the opponent. Mini-game thought to develop the "tech-tree" learning algorithm . 6 | 7 | Find bellow an image regarding Protoss tech development 8 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/new_minigames/BlueMoon/Images/Protoss.jpg) 9 | 10 | In this first iteration, as It is consider a master-branch, there are two games proposed with Zealots and Stalkers / Sentry development . 11 | 12 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/new_minigames/BlueMoon/Images/First_Tech_Core_Protoss.png) 13 | 14 | #### Initial State 15 | 16 | FIRST GAME 17 | 18 | ### Initial State 19 | Production development : 50000 minerals and vespene 20 | * 10 supply depots 21 | * 1 Barracks 22 | * 1 Factory 23 | * 1 Starport 24 | * 3 Zealots (Protoss) 25 | * 6 Marines (Terran) 26 | 27 | ### Rewards 28 | 29 | * Protoss defeated: +10 30 | * Terran defeated: -10 31 | 32 | ### Second Wave 33 | 34 | 35 | 36 | #### Tech developments in melees 37 | 38 | 39 | 40 | #### mini-game under development 41 | 42 | Please note this mini-game is under development and It´s a second iteration 43 | If you find any issue you are encouraged to open an issue or send a pull request following the code of conduct. 44 | -------------------------------------------------------------------------------- /new_minigames/BlueMoon/bluemoon.SC2Map: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/BlueMoon/bluemoon.SC2Map -------------------------------------------------------------------------------- /new_minigames/FlowerFields/FlowerFields.SC2Map: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/FlowerFields/FlowerFields.SC2Map -------------------------------------------------------------------------------- /new_minigames/FlowerFields/README.md: -------------------------------------------------------------------------------- 1 | ## FlowerFields 2 | 3 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/Captura%20de%20pantalla%202018-01-02%20a%20las%2014.51.09.png) 4 | 5 | 6 | #### Description 7 | 8 | Defeat protoss photon cannon without losing any marauder. 9 | The goal of the minigame is to learn to regroup marauders and to attack in a coordinated way without losing any unit 10 | 11 | #### Initial State 12 | 13 | * 4 photon cannon at Central playable size 14 | * 2 marauders at right playable size 15 | * 2 marauders at left playable size 16 | * 1 medievac 17 | 18 | #### Rewards 19 | 20 | Protoss defeated : +10 21 | Terran defeated : -5 22 | 23 | #### End Conditions 24 | 25 | Time elapsed 26 | Protoss defeated 27 | Time Limit : 60 seconds 28 | 29 | 30 | #### Additional Notes 31 | 32 | Terrain condition designed for photon defense game development 33 | Fog of war disabled 34 | No camera movement required (single-screen) 35 | Note that this map is under development and should be re-sized for balance 36 | Please note this work is still under development. If you find any relevant comment or issue feel free to open an issue 37 | 38 | 39 | 40 | ### FlowerFields random agent running 41 | 42 | --Clone the repo 43 | 44 | --Put FlowerFields.sc2 map into your minigames map folder 45 | 46 | --Go to pysc2/maps/mini_games.py and add FlowerFields map to the array map 47 | 48 | --In the /pysc2/agents/ folder type 49 | 50 | ``` 51 | $ python3 python3 -m pysc2.bin.agent --map FlowerFields 52 | ``` 53 | -------------------------------------------------------------------------------- /new_minigames/HallucinIceSMAC/HallucinIce.SC2Map: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/HallucinIceSMAC/HallucinIce.SC2Map -------------------------------------------------------------------------------- /new_minigames/HallucinIceSMAC/README.md: -------------------------------------------------------------------------------- 1 | HallucinIce Version for SMAC StarCraft II Multiagent environment. 2 | [here](https://github.com/SoyGema/smac) 3 | -------------------------------------------------------------------------------- /new_minigames/MicroPrism/README.MD: -------------------------------------------------------------------------------- 1 | 2 | 3 | ## MicroPrism 4 | by @SoyGema and @Evan-Kim2028 5 | 6 | 7 | #### Description 8 | The mini-game is a challenge of learning the use of Warp Prism by micro in a Protoss VS Protoss Melee 9 | 10 | #### Initial State 11 | 12 | * 2 Stalkers + 1 Warp Prism 13 | * 2 Stalkers 14 | 15 | #### Rewards 16 | 17 | Protoss defeated : +10 18 | 19 | #### End Conditions 20 | 21 | Time elapsed 22 | Protoss defeated 23 | 24 | #### Time Limit 25 | 26 | 45 seconds 27 | 28 | #### Additional Notes 29 | 30 | Fog of war disabled 31 | No camera movement required (single-screen) 32 | Note that this map is under development and should be re-sized for balance 33 | Please note this work is still under development. If you find any relevant comment or issue feel free to open an issue 34 | 35 | #### Intended machine Learning objetive 36 | The behaviour that will separate good agents from bad ones : bad agents will micro without using the Warp Prism or might use Warp Prism as a bait for the enemy 37 | -------------------------------------------------------------------------------- /new_minigames/RedWaves/README.md: -------------------------------------------------------------------------------- 1 | 2 | #### Description 3 | The mini-game is a challenge for defending against waves of zergs 4 | Choose your favorite race and defend against incremental zerg attacks 5 | 6 | #### Initial State 7 | Choose the race you will be by typing in the chat message bar : terran (for Terran) , zerg (for zerg) or protoss (for protoss) 8 | 9 | if choose playing with Terran, your initial conditions will be 10 | * 1 Command center 11 | * 10 SCV 12 | * 50 minerals 13 | 14 | if choose playing with zerg, your initial conditions will be 15 | * 1 Hive 16 | * 10 drone 17 | * 50 minerals 18 | 19 | if choose playing with zerg, your initial conditions will be 20 | * 1 Nexus 21 | * 10 Probe 22 | * 50 minerals 23 | 24 | #### Wave description 25 | * 1st wave : 6 zerlings 26 | * 2nd wave : 4 changeling with wings 27 | * 3rd wave : 4 roaches 28 | * 4th wave : 4 roaches (upgraded) + 4 changelling 29 | * 5th wave : 6 changelling (upgraded) 30 | * 6th wave : 4 roaches (upgraded) + 4 changelling (upgraded) 31 | * 7th wave : 6 hydralisks 32 | * 8th wave : 4 mutas 33 | * 9th wave : 4 mutas + 4 roaches 34 | * 10th wave : 1 Ultralisk 35 | 36 | #### Rewards 37 | 38 | * units dead in zerg group : +10 39 | 40 | #### End Conditions 41 | 42 | * Time elapsed 43 | * Zerg defeated 44 | 45 | #### Time in between each wave 46 | 47 | * 30, 45 and/or 60 seconds , depending on the wave 48 | 49 | #### Additional Notes 50 | 51 | * Note that this map is under development and should be re-sized for balance 52 | * Please note this work is still under development. If you find any relevant comment or issue feel free to open an issue 53 | 54 | 55 | #### Intended machine Learning objetive 56 | Find the optimal unit defense positions nad units to defend against attack zerg waves. 57 | The behaviour that will separate good agents from bad ones : bad agents will produce a random army. Good agents will try to 58 | get a balance army including air . 59 | -------------------------------------------------------------------------------- /new_minigames/RedWaves/redwaves.SC2Map: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/RedWaves/redwaves.SC2Map -------------------------------------------------------------------------------- /new_minigames/SentryDefense/README.md: -------------------------------------------------------------------------------- 1 | ## Sentry Defense mini-game Map 2 | 3 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/Captura%20de%20pantalla%202017-09-18%20a%20las%2020.14.14.png) 4 | 5 | #### Description 6 | The mini-game is a melee in between Terran marauders and marines and Protoss Stalker and Sentry units on opposite sides. 7 | The goal for agent research is to maximize the utility -explotation- of sentry unit defense, hallucination and sield unit functions 8 | Both armys are disposed in two playable spaces with Arrowhead formation, presented as an end of movement phase. 9 | Sentry unit is disposed as Leader with special weapon position. 10 | 11 | 12 | 13 | #### Initial State 14 | 15 | * 7 Marines at left playable size 16 | * 4 Marauders at left playable size disposed as leader in arrowhead formation 17 | * 3 Stalkers at right playable size 18 | * 4 Sentry at right playable size disposed as leader in arrowhead formation 19 | 20 | #### Rewards 21 | 22 | * Protoss defeated : -1 23 | * Terran defeated : +10 24 | 25 | #### End Conditions 26 | 27 | * Time elapsed 28 | * Terran defeated 29 | 30 | #### Time Limit 31 | 32 | * 180 seconds 33 | 34 | #### Additional Notes 35 | 36 | * Fog of war disabled 37 | * No camera movement required (single-screen) 38 | * Note that this map is under development and should be re-sized for balance 39 | * Please note this work is still under development. If you find any relevant comment or issue feel free to open an issue 40 | 41 | #### Tutorial 42 | Find a tutorial about another minigame at https://soygema.github.io/Startcraft_pysc2_minigames/ 43 | This map is currently under development and balancing . 44 | 45 | 46 | Next upload includes : 47 | --Player 1 as Protoss 48 | --New reward system 49 | --Seized Playable spaces 50 | 51 | 52 | -------------------------------------------------------------------------------- /new_minigames/SentryDefense/SentryDefense.SC2Map: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/SentryDefense/SentryDefense.SC2Map -------------------------------------------------------------------------------- /new_minigames/SentryForceField/ForceField.SC2Map: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/SentryForceField/ForceField.SC2Map -------------------------------------------------------------------------------- /new_minigames/SentryForceField/README.md: -------------------------------------------------------------------------------- 1 | ## ForceField 2 | 3 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/ForceField2.png) 4 | 5 | #### Description 6 | 7 | The mini-game is an imbalanced melee in between Zerg ans Prottoss. 8 | The goal is to exploit sentry forcefield function 9 | 10 | #### Initial State 11 | 12 | * 2 Sentry at left playable size 13 | * 3 Hydralysks at right playable size 14 | * 2 Zerlings at right playable size 15 | 16 | 17 | #### Rewards 18 | 19 | Protoss defeated : -10 20 | Zerg defeated : +10 21 | 22 | #### End Conditions 23 | 24 | Time elapsed 25 | Zerg defeated 26 | Time Limit 27 | 28 | 180 seconds 29 | #### Additional Notes 30 | Terrain condition designed for forcefield game development 31 | Fog of war disabled 32 | No camera movement required (single-screen) 33 | Note that this map is under development and should be re-sized for balance 34 | Please note this work is still under development. If you find any relevant comment or issue feel free to open an issue 35 | 36 | # Sentry Agent mini-game Map exploration for Forcefield map 37 | 38 | This part of the repository aims to post several agents regarding functions of sentry unit. 39 | SentryDefense.py --contains all the actions for sentrydefense unit 40 | scripted_agent.py --contains tests for Forcefield Starcraft 2 map 41 | 42 | ### Sentry unit scripted bot running 43 | 44 | --Clone the repo 45 | 46 | --Put ForceField.sc2 map into your minigames map folder 47 | 48 | --Go to pysc2/maps/mini_games.py and add ForceField map to the array map 49 | 50 | --In the /pysc2/agents/ folder type 51 | 52 | ``` 53 | $ python3 -m pysc2.bin.agent --agent scripted_agent.SentryForceField --map ForceField 54 | ``` 55 | -------------------------------------------------------------------------------- /new_minigames/SentryForceField/scripted_agent.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 Google Inc. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS-IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | # Thanks to : jmathison 16 | # Simon0xzx 17 | 18 | """Scripted agents.""" 19 | 20 | from __future__ import absolute_import 21 | from __future__ import division 22 | from __future__ import print_function 23 | 24 | import numpy 25 | 26 | from pysc2.agents import base_agent 27 | from pysc2.lib import actions 28 | from pysc2.lib import features 29 | 30 | _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index 31 | _PLAYER_FRIENDLY = 1 32 | _PLAYER_NEUTRAL = 3 # beacon/minerals 33 | _PLAYER_HOSTILE = 4 34 | _NO_OP = actions.FUNCTIONS.no_op.id 35 | _MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id 36 | _ATTACK_SCREEN = actions.FUNCTIONS.Attack_screen.id 37 | _SELECT_ARMY = actions.FUNCTIONS.select_army.id 38 | _NOT_QUEUED = [0] 39 | _SELECT_ALL = [0] 40 | _HAL_ADEPT = actions.FUNCTIONS.Hallucination_Adept_quick.id 41 | _HAL_ARCHON = actions.FUNCTIONS.Hallucination_Archon_quick.id 42 | _HAL_COL = actions.FUNCTIONS.Hallucination_Colossus_quick.id 43 | _HAL_DISRUP = actions.FUNCTIONS.Hallucination_Disruptor_quick.id 44 | _HAL_HIGTEM = actions.FUNCTIONS.Hallucination_HighTemplar_quick.id 45 | _HAL_IMN = actions.FUNCTIONS.Hallucination_Immortal_quick.id 46 | _HAL_PHOENIX = actions.FUNCTIONS.Hallucination_Phoenix_quick.id 47 | _HAL_STALKER = actions.FUNCTIONS.Hallucination_Stalker_quick.id 48 | _HAL_VOIDRAID = actions.FUNCTIONS.Hallucination_VoidRay_quick.id 49 | _HAL_ZEALOT = actions.FUNCTIONS.Hallucination_Zealot_quick.id 50 | _FORCE_FIELD = actions.FUNCTIONS.Effect_ForceField_screen.id 51 | _GUARD_FIELD = actions.FUNCTIONS.Effect_GuardianShield_quick.id 52 | 53 | Hallucinations = [_HAL_ADEPT, _HAL_ARCHON, _HAL_DISRUP, _HAL_HIGTEM, _HAL_IMN, _HAL_PHOENIX, _HAL_STALKER, _HAL_VOIDRAID, _HAL_ZEALOT] 54 | 55 | class Hallucination(base_agent.BaseAgent): 56 | """An agent specifically for solving the ForceField map.""" 57 | 58 | def step(self, obs): 59 | super(Hallucination, self).step(obs) 60 | if Hallucinations in obs.observation["available_actions"]: 61 | player_relative = obs.observation["screen"][_PLAYER_RELATIVE] 62 | hellion_y, hellion_x = (player_relative == _PLAYER_HOSTILE).nonzero() 63 | if not hydralisk_y.any(): 64 | return actions.FunctionCall(_NO_OP, []) 65 | index = numpy.argmax(hydralisk_y) 66 | target = [hydralisk_x[index], hydralisk_y[index]] 67 | return actions.FunctionCall(Hallucinations(np.random.randint(0,len(Hallucinations)), [_NOT_QUEUED])) 68 | elif _SELECT_ARMY in obs.observation["available_actions"]: 69 | return actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL]) 70 | else: 71 | return actions.FunctionCall(_NO_OP, []) 72 | 73 | class SentryForceField(base_agent.BaseAgent): 74 | """An agent specifically for solving the ForceField map.""" 75 | 76 | def step(self, obs): 77 | super(SentryForceField, self).step(obs) 78 | if _FORCE_FIELD= in obs.observation["available_actions"]: 79 | player_relative = obs.observation["screen"][_PLAYER_RELATIVE] 80 | hydralisk_y, hydralisk_x = (player_relative == _PLAYER_HOSTILE).nonzero() 81 | if not hydralisk_y.any(): 82 | return actions.FunctionCall(_NO_OP, []) 83 | index = numpy.argmax(hydralisk_y) 84 | target = [hydralisk_x[index], hydralisk_y[index]] 85 | return actions.FunctionCall(_FORCE_FIELD, [_NOT_QUEUED, target]) 86 | elif _SELECT_ARMY in obs.observation["available_actions"]: 87 | return actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL]) 88 | else: 89 | return actions.FunctionCall(_NO_OP, []) 90 | -------------------------------------------------------------------------------- /new_minigames/SentryHallucination/HallucinIce.SC2Map: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/SentryHallucination/HallucinIce.SC2Map -------------------------------------------------------------------------------- /new_minigames/SentryHallucination/README.md: -------------------------------------------------------------------------------- 1 | 2 | ## HallucinIce (Beta) 3 | 4 | The purpose of these life-like images is twofold: 5 | * making the enemies of the protoss believe they’re fighting a much larger force than anticipated 6 | * protecting protoss lives 7 | 8 | This map is under development. Last version provided soon. 9 | Agent trained 10 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/HallucinIce.gif) 11 | 12 | ![alt tag](https://github.com/SoyGema/Startcraft_pysc2_minigames/blob/master/Images/HallucinIceV2.png) 13 | 14 | #### Description 15 | 16 | The mini-game is an imbalanced melee in between Terran and Prottoss. 17 | The goal is to exploit sentry hallucination function 18 | 19 | #### Initial State 20 | 21 | * 4 Sentry at left playable size 22 | * 4 Hellions at right playable size 23 | * 2 Reapers at right playable size 24 | 25 | 26 | #### Rewards 27 | 28 | Protoss defeated : -10 29 | Terran defeated : +10 30 | 31 | #### End Conditions 32 | 33 | Time elapsed 34 | Zerg defeated 35 | Time Limit 36 | 37 | 180 seconds 38 | #### Additional Notes 39 | Terrain condition designed for hallucination defense game development 40 | Fog of war disabled 41 | No camera movement required (single-screen) 42 | Note that this map is under development and should be re-sized for balance 43 | Please note this work is still under development. If you find any relevant comment or issue feel free to open an issue 44 | 45 | 46 | # Sentry Agent mini-game Map exploration for HallucinIce map 47 | 48 | This part of the repository aims to post several agents regarding functions of sentry unit. 49 | SentryDefense.py --contains all the actions for sentrydefense unit 50 | scripted_agent.py --contains tests for Forcefield Starcraft 2 map 51 | 52 | ### HallucinIce scripted bot ARCHON running 53 | 54 | --Clone the repo 55 | 56 | --Put HallucinIce.sc2 map into your minigames map folder 57 | 58 | --Go to pysc2/maps/mini_games.py and add HallucinIce map to the array map 59 | 60 | --In the /pysc2/agents/ folder type 61 | 62 | ``` 63 | $ python3 -m pysc2.bin.agent --agent scripted_agent.HallucinationArchon --map ForceField 64 | ``` 65 | -------------------------------------------------------------------------------- /new_minigames/SentryHallucination/scripted_agent.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 Google Inc. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS-IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | """Scripted agents.""" 15 | 16 | from __future__ import absolute_import 17 | from __future__ import division 18 | from __future__ import print_function 19 | 20 | import numpy 21 | 22 | from pysc2.agents import base_agent 23 | from pysc2.lib import actions 24 | from pysc2.lib import features 25 | 26 | _PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index 27 | _PLAYER_FRIENDLY = 1 28 | _PLAYER_NEUTRAL = 3 # beacon/minerals 29 | _PLAYER_HOSTILE = 4 30 | _NO_OP = actions.FUNCTIONS.no_op.id 31 | _MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id 32 | _ATTACK_SCREEN = actions.FUNCTIONS.Attack_screen.id 33 | _SELECT_ARMY = actions.FUNCTIONS.select_army.id 34 | _NOT_QUEUED = [0] 35 | _SELECT_ALL = [0] 36 | _HAL_ADEPT = actions.FUNCTIONS.Hallucination_Adept_quick.id 37 | _HAL_ARCHON = actions.FUNCTIONS.Hallucination_Archon_quick.id 38 | _HAL_COL = actions.FUNCTIONS.Hallucination_Colossus_quick.id 39 | _HAL_DISRUP = actions.FUNCTIONS.Hallucination_Disruptor_quick.id 40 | _HAL_HIGTEM = actions.FUNCTIONS.Hallucination_HighTemplar_quick.id 41 | _HAL_IMN = actions.FUNCTIONS.Hallucination_Immortal_quick.id 42 | _HAL_PHOENIX = actions.FUNCTIONS.Hallucination_Phoenix_quick.id 43 | _HAL_STALKER = actions.FUNCTIONS.Hallucination_Stalker_quick.id 44 | _HAL_VOIDRAID = actions.FUNCTIONS.Hallucination_VoidRay_quick.id 45 | _HAL_ZEALOT = actions.FUNCTIONS.Hallucination_Zealot_quick.id 46 | _FORCE_FIELD = actions.FUNCTIONS.Effect_ForceField_screen.id 47 | _GUARD_FIELD = actions.FUNCTIONS.Effect_GuardianShield_quick.id 48 | 49 | 50 | 51 | 52 | class SentryForceField(base_agent.BaseAgent): 53 | """An agent specifically for solving the ForceField map.""" 54 | 55 | def step(self, obs): 56 | super(SentryForceField, self).step(obs) 57 | if _FORCE_FIELD in obs.observation["available_actions"]: 58 | player_relative = obs.observation["screen"][_PLAYER_RELATIVE] 59 | hydralisk_y, hydralisk_x = (player_relative == _PLAYER_HOSTILE).nonzero() 60 | if not hydralisk_y.any(): 61 | return actions.FunctionCall(_NO_OP, []) 62 | index = numpy.argmax(hydralisk_y) 63 | target = [hydralisk_x[index], hydralisk_y[index]] 64 | return actions.FunctionCall(_FORCE_FIELD, [_NOT_QUEUED, target]) 65 | elif _SELECT_ARMY in obs.observation["available_actions"]: 66 | return actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL]) 67 | else: 68 | return actions.FunctionCall(_NO_OP, []) 69 | 70 | Hallucinations = [_HAL_ADEPT, _HAL_ARCHON, _HAL_DISRUP, _HAL_HIGTEM, _HAL_IMN, _HAL_PHOENIX, _HAL_STALKER, _HAL_VOIDRAID, _HAL_ZEALOT] 71 | 72 | class HallucinationArchon(base_agent.BaseAgent): 73 | """An agent specifically for solving the ForceField map.""" 74 | 75 | def step(self, obs): 76 | super(Hallucination, self).step(obs) 77 | if _HAL_ARCHON in obs.observation["available_actions"]: 78 | player_relative = obs.observation["screen"][_PLAYER_RELATIVE] 79 | hellion_y, hellion_x = (player_relative == _PLAYER_HOSTILE).nonzero() 80 | if not hellion_y.any(): 81 | return actions.FunctionCall(_NO_OP, []) 82 | index = numpy.argmax(hellion_y) 83 | target = [hellion_x[index], hellion_y[index]] 84 | return actions.FunctionCall(_HAL_ARCHON, [_NOT_QUEUED]) 85 | elif _SELECT_ARMY in obs.observation["available_actions"]: 86 | return actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL]) 87 | else: 88 | return actions.FunctionCall(_NO_OP, []) 89 | 90 | -------------------------------------------------------------------------------- /new_minigames/TheRightPath/README.md: -------------------------------------------------------------------------------- 1 | #### Description 2 | The mini-game is a problem of finding the optimal path in order to move a marine into a beacon, picking up minerals and avoiding mines 3 | Each time a marine moves to the beacon the becon changes its position 4 | 5 | 6 | #### Initial State 7 | 8 | * 1 Marine 9 | * 1 Beacon 10 | * 4 mines randomly distributed 11 | * 20 minerals shards to collect 12 | 13 | #### Rewards 14 | 15 | * Mineral collected : +1 16 | * Beacon reached : +10 17 | 18 | #### End Conditions 19 | 20 | * Time elapsed 21 | * Terran defeated 22 | 23 | #### Time Limit 24 | 25 | * 60 seconds 26 | 27 | #### Additional Notes 28 | 29 | * Fog of war disabled 30 | * No camera movement required (single-screen) 31 | * Note that this map is under development and should be re-sized for balance 32 | * Please note this work is still under development. If you find any relevant comment or issue feel free to open an issue 33 | 34 | 35 | #### Intended machine Learning objetive 36 | Find the optimal path to the beacon while avoiding death 37 | The behaviour that will separate good agents from bad ones : bad agents will go strictly to the beacon. Good agents will try to 38 | get minerals while getting there . 39 | 40 | 41 | kudos : @flipper83 42 | -------------------------------------------------------------------------------- /new_minigames/TheRightPath/TheRightPath.SC2Map: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/new_minigames/TheRightPath/TheRightPath.SC2Map -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | PySC2==2.0 2 | numpy==1.14.0 3 | Keras==2.2.2 4 | Keras-Applications==1.0.4 5 | keras-contrib==2.0.8 6 | Keras-Preprocessing==1.0.2 7 | keras-rl==0.4.2 8 | pandas==0.22.0 9 | -------------------------------------------------------------------------------- /zip/README.md: -------------------------------------------------------------------------------- 1 | Folder that contains zip folder of mini-games 2 | -------------------------------------------------------------------------------- /zip/mini-games.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SoyGema/Startcraft_pysc2_minigames/dc0ece73a98cc3419757df1ee39d3fbea07270b1/zip/mini-games.zip --------------------------------------------------------------------------------