├── .gitignore ├── A2C ├── A2C_Continuous.py └── A2C_Discrete.py ├── A3C ├── A3C_Continuous.py └── A3C_Discrete.py ├── DDPG └── DDPG_Continuous.py ├── DQN └── DQN_Discrete.py ├── DRQN └── DRQN_Discrete.py ├── DoubleDQN └── DoubleDQN_Discrete.py ├── DuelingDQN └── DuelingDQN_Discrete.py ├── DuelingDoubleDQN └── DuelingDoubleDQN_Discrete.py ├── LICENSE ├── PPO ├── PPO_Continuous.py └── PPO_Discrete.py ├── README.md └── assets ├── .DS_Store ├── cartpolev1.svg ├── discrete_reward_plot.png └── logo.png /.gitignore: -------------------------------------------------------------------------------- 1 | **/__pycache__ 2 | **/.DS_Store/ 3 | **/.vscode/ 4 | **/wandb -------------------------------------------------------------------------------- /A2C/A2C_Continuous.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense, Lambda 4 | 5 | import gym 6 | import argparse 7 | import numpy as np 8 | 9 | tf.keras.backend.set_floatx('float64') 10 | 11 | wandb.init(name='A2C', project="deep-rl-tf2") 12 | 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument('--gamma', type=float, default=0.99) 15 | parser.add_argument('--update_interval', type=int, default=5) 16 | parser.add_argument('--actor_lr', type=float, default=0.0005) 17 | parser.add_argument('--critic_lr', type=float, default=0.001) 18 | 19 | args = parser.parse_args() 20 | 21 | 22 | class Actor: 23 | def __init__(self, state_dim, action_dim, action_bound, std_bound): 24 | self.state_dim = state_dim 25 | self.action_dim = action_dim 26 | self.action_bound = action_bound 27 | self.std_bound = std_bound 28 | self.model = self.create_model() 29 | self.opt = tf.keras.optimizers.Adam(args.actor_lr) 30 | 31 | def create_model(self): 32 | state_input = Input((self.state_dim,)) 33 | dense_1 = Dense(32, activation='relu')(state_input) 34 | dense_2 = Dense(32, activation='relu')(dense_1) 35 | out_mu = Dense(self.action_dim, activation='tanh')(dense_2) 36 | mu_output = Lambda(lambda x: x * self.action_bound)(out_mu) 37 | std_output = Dense(self.action_dim, activation='softplus')(dense_2) 38 | return tf.keras.models.Model(state_input, [mu_output, std_output]) 39 | 40 | def get_action(self, state): 41 | state = np.reshape(state, [1, self.state_dim]) 42 | mu, std = self.model.predict(state) 43 | mu, std = mu[0], std[0] 44 | return np.random.normal(mu, std, size=self.action_dim) 45 | 46 | def log_pdf(self, mu, std, action): 47 | std = tf.clip_by_value(std, self.std_bound[0], self.std_bound[1]) 48 | var = std ** 2 49 | log_policy_pdf = -0.5 * (action - mu) ** 2 / \ 50 | var - 0.5 * tf.math.log(var * 2 * np.pi) 51 | return tf.reduce_sum(log_policy_pdf, 1, keepdims=True) 52 | 53 | def compute_loss(self, mu, std, actions, advantages): 54 | log_policy_pdf = self.log_pdf(mu, std, actions) 55 | loss_policy = log_policy_pdf * advantages 56 | return tf.reduce_sum(-loss_policy) 57 | 58 | def train(self, states, actions, advantages): 59 | with tf.GradientTape() as tape: 60 | mu, std = self.model(states, training=True) 61 | loss = self.compute_loss(mu, std, actions, advantages) 62 | grads = tape.gradient(loss, self.model.trainable_variables) 63 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 64 | return loss 65 | 66 | 67 | class Critic: 68 | def __init__(self, state_dim): 69 | self.state_dim = state_dim 70 | self.model = self.create_model() 71 | self.opt = tf.keras.optimizers.Adam(args.critic_lr) 72 | 73 | def create_model(self): 74 | return tf.keras.Sequential([ 75 | Input((self.state_dim,)), 76 | Dense(32, activation='relu'), 77 | Dense(32, activation='relu'), 78 | Dense(16, activation='relu'), 79 | Dense(1, activation='linear') 80 | ]) 81 | 82 | def compute_loss(self, v_pred, td_targets): 83 | mse = tf.keras.losses.MeanSquaredError() 84 | return mse(td_targets, v_pred) 85 | 86 | def train(self, states, td_targets): 87 | with tf.GradientTape() as tape: 88 | v_pred = self.model(states, training=True) 89 | assert v_pred.shape == td_targets.shape 90 | loss = self.compute_loss(v_pred, tf.stop_gradient(td_targets)) 91 | grads = tape.gradient(loss, self.model.trainable_variables) 92 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 93 | return loss 94 | 95 | 96 | class Agent: 97 | def __init__(self, env): 98 | self.env = env 99 | self.state_dim = self.env.observation_space.shape[0] 100 | self.action_dim = self.env.action_space.shape[0] 101 | self.action_bound = self.env.action_space.high[0] 102 | self.std_bound = [1e-2, 1.0] 103 | 104 | self.actor = Actor(self.state_dim, self.action_dim, 105 | self.action_bound, self.std_bound) 106 | self.critic = Critic(self.state_dim) 107 | 108 | def td_target(self, reward, next_state, done): 109 | if done: 110 | return reward 111 | v_value = self.critic.model.predict( 112 | np.reshape(next_state, [1, self.state_dim])) 113 | return np.reshape(reward + args.gamma * v_value[0], [1, 1]) 114 | 115 | def advatnage(self, td_targets, baselines): 116 | return td_targets - baselines 117 | 118 | def list_to_batch(self, list): 119 | batch = list[0] 120 | for elem in list[1:]: 121 | batch = np.append(batch, elem, axis=0) 122 | return batch 123 | 124 | def train(self, max_episodes=1000): 125 | for ep in range(max_episodes): 126 | state_batch = [] 127 | action_batch = [] 128 | td_target_batch = [] 129 | advatnage_batch = [] 130 | episode_reward, done = 0, False 131 | 132 | state = self.env.reset() 133 | 134 | while not done: 135 | # self.env.render() 136 | action = self.actor.get_action(state) 137 | action = np.clip(action, -self.action_bound, self.action_bound) 138 | 139 | next_state, reward, done, _ = self.env.step(action) 140 | 141 | state = np.reshape(state, [1, self.state_dim]) 142 | action = np.reshape(action, [1, self.action_dim]) 143 | next_state = np.reshape(next_state, [1, self.state_dim]) 144 | reward = np.reshape(reward, [1, 1]) 145 | 146 | td_target = self.td_target((reward+8)/8, next_state, done) 147 | advantage = self.advatnage( 148 | td_target, self.critic.model.predict(state)) 149 | 150 | state_batch.append(state) 151 | action_batch.append(action) 152 | td_target_batch.append(td_target) 153 | advatnage_batch.append(advantage) 154 | 155 | if len(state_batch) >= args.update_interval or done: 156 | states = self.list_to_batch(state_batch) 157 | actions = self.list_to_batch(action_batch) 158 | td_targets = self.list_to_batch(td_target_batch) 159 | advantages = self.list_to_batch(advatnage_batch) 160 | 161 | actor_loss = self.actor.train(states, actions, advantages) 162 | critic_loss = self.critic.train(states, td_targets) 163 | 164 | state_batch = [] 165 | action_batch = [] 166 | td_target_batch = [] 167 | advatnage_batch = [] 168 | 169 | episode_reward += reward[0][0] 170 | state = next_state[0] 171 | 172 | print('EP{} EpisodeReward={}'.format(ep, episode_reward)) 173 | wandb.log({'Reward': episode_reward}) 174 | 175 | 176 | def main(): 177 | env_name = 'Pendulum-v0' 178 | env = gym.make(env_name) 179 | agent = Agent(env) 180 | agent.train() 181 | 182 | 183 | if __name__ == "__main__": 184 | main() 185 | -------------------------------------------------------------------------------- /A2C/A2C_Discrete.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense 4 | 5 | import gym 6 | import argparse 7 | import numpy as np 8 | 9 | tf.keras.backend.set_floatx('float64') 10 | 11 | wandb.init(name='A2C', project="deep-rl-tf2") 12 | 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument('--gamma', type=float, default=0.99) 15 | parser.add_argument('--update_interval', type=int, default=5) 16 | parser.add_argument('--actor_lr', type=float, default=0.0005) 17 | parser.add_argument('--critic_lr', type=float, default=0.001) 18 | 19 | args = parser.parse_args() 20 | 21 | 22 | class Actor: 23 | def __init__(self, state_dim, action_dim): 24 | self.state_dim = state_dim 25 | self.action_dim = action_dim 26 | self.model = self.create_model() 27 | self.opt = tf.keras.optimizers.Adam(args.actor_lr) 28 | 29 | def create_model(self): 30 | return tf.keras.Sequential([ 31 | Input((self.state_dim,)), 32 | Dense(32, activation='relu'), 33 | Dense(16, activation='relu'), 34 | Dense(self.action_dim, activation='softmax') 35 | ]) 36 | 37 | def compute_loss(self, actions, logits, advantages): 38 | ce_loss = tf.keras.losses.SparseCategoricalCrossentropy( 39 | from_logits=True) 40 | actions = tf.cast(actions, tf.int32) 41 | policy_loss = ce_loss( 42 | actions, logits, sample_weight=tf.stop_gradient(advantages)) 43 | return policy_loss 44 | 45 | def train(self, states, actions, advantages): 46 | with tf.GradientTape() as tape: 47 | logits = self.model(states, training=True) 48 | loss = self.compute_loss( 49 | actions, logits, advantages) 50 | grads = tape.gradient(loss, self.model.trainable_variables) 51 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 52 | return loss 53 | 54 | 55 | class Critic: 56 | def __init__(self, state_dim): 57 | self.state_dim = state_dim 58 | self.model = self.create_model() 59 | self.opt = tf.keras.optimizers.Adam(args.critic_lr) 60 | 61 | def create_model(self): 62 | return tf.keras.Sequential([ 63 | Input((self.state_dim,)), 64 | Dense(32, activation='relu'), 65 | Dense(16, activation='relu'), 66 | Dense(16, activation='relu'), 67 | Dense(1, activation='linear') 68 | ]) 69 | 70 | def compute_loss(self, v_pred, td_targets): 71 | mse = tf.keras.losses.MeanSquaredError() 72 | return mse(td_targets, v_pred) 73 | 74 | def train(self, states, td_targets): 75 | with tf.GradientTape() as tape: 76 | v_pred = self.model(states, training=True) 77 | assert v_pred.shape == td_targets.shape 78 | loss = self.compute_loss(v_pred, tf.stop_gradient(td_targets)) 79 | grads = tape.gradient(loss, self.model.trainable_variables) 80 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 81 | return loss 82 | 83 | 84 | class Agent: 85 | def __init__(self, env): 86 | self.env = env 87 | self.state_dim = self.env.observation_space.shape[0] 88 | self.action_dim = self.env.action_space.n 89 | self.actor = Actor(self.state_dim, self.action_dim) 90 | self.critic = Critic(self.state_dim) 91 | 92 | def td_target(self, reward, next_state, done): 93 | if done: 94 | return reward 95 | v_value = self.critic.model.predict( 96 | np.reshape(next_state, [1, self.state_dim])) 97 | return np.reshape(reward + args.gamma * v_value[0], [1, 1]) 98 | 99 | def advatnage(self, td_targets, baselines): 100 | return td_targets - baselines 101 | 102 | def list_to_batch(self, list): 103 | batch = list[0] 104 | for elem in list[1:]: 105 | batch = np.append(batch, elem, axis=0) 106 | return batch 107 | 108 | def train(self, max_episodes=1000): 109 | for ep in range(max_episodes): 110 | state_batch = [] 111 | action_batch = [] 112 | td_target_batch = [] 113 | advatnage_batch = [] 114 | episode_reward, done = 0, False 115 | 116 | state = self.env.reset() 117 | 118 | while not done: 119 | # self.env.render() 120 | probs = self.actor.model.predict( 121 | np.reshape(state, [1, self.state_dim])) 122 | action = np.random.choice(self.action_dim, p=probs[0]) 123 | 124 | next_state, reward, done, _ = self.env.step(action) 125 | 126 | state = np.reshape(state, [1, self.state_dim]) 127 | action = np.reshape(action, [1, 1]) 128 | next_state = np.reshape(next_state, [1, self.state_dim]) 129 | reward = np.reshape(reward, [1, 1]) 130 | 131 | td_target = self.td_target(reward * 0.01, next_state, done) 132 | advantage = self.advatnage( 133 | td_target, self.critic.model.predict(state)) 134 | 135 | state_batch.append(state) 136 | action_batch.append(action) 137 | td_target_batch.append(td_target) 138 | advatnage_batch.append(advantage) 139 | 140 | if len(state_batch) >= args.update_interval or done: 141 | states = self.list_to_batch(state_batch) 142 | actions = self.list_to_batch(action_batch) 143 | td_targets = self.list_to_batch(td_target_batch) 144 | advantages = self.list_to_batch(advatnage_batch) 145 | 146 | actor_loss = self.actor.train(states, actions, advantages) 147 | critic_loss = self.critic.train(states, td_targets) 148 | 149 | state_batch = [] 150 | action_batch = [] 151 | td_target_batch = [] 152 | advatnage_batch = [] 153 | 154 | episode_reward += reward[0][0] 155 | state = next_state[0] 156 | 157 | print('EP{} EpisodeReward={}'.format(ep, episode_reward)) 158 | wandb.log({'Reward': episode_reward}) 159 | 160 | 161 | def main(): 162 | env_name = 'CartPole-v1' 163 | env = gym.make(env_name) 164 | agent = Agent(env) 165 | agent.train() 166 | 167 | 168 | if __name__ == "__main__": 169 | main() 170 | -------------------------------------------------------------------------------- /A3C/A3C_Continuous.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense, Lambda 4 | 5 | import gym 6 | import argparse 7 | import numpy as np 8 | from threading import Thread 9 | from multiprocessing import cpu_count 10 | tf.keras.backend.set_floatx('float64') 11 | wandb.init(name='A3C', project="deep-rl-tf2") 12 | 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument('--gamma', type=float, default=0.99) 15 | parser.add_argument('--update_interval', type=int, default=5) 16 | parser.add_argument('--actor_lr', type=float, default=0.0005) 17 | parser.add_argument('--critic_lr', type=float, default=0.001) 18 | 19 | args = parser.parse_args() 20 | 21 | CUR_EPISODE = 0 22 | 23 | class Actor: 24 | def __init__(self, state_dim, action_dim, action_bound, std_bound): 25 | self.state_dim = state_dim 26 | self.action_dim = action_dim 27 | self.action_bound = action_bound 28 | self.std_bound = std_bound 29 | self.model = self.create_model() 30 | self.opt = tf.keras.optimizers.Adam(args.actor_lr) 31 | self.entropy_beta = 0.01 32 | 33 | def create_model(self): 34 | state_input = Input((self.state_dim,)) 35 | dense_1 = Dense(32, activation='relu')(state_input) 36 | dense_2 = Dense(32, activation='relu')(dense_1) 37 | out_mu = Dense(self.action_dim, activation='tanh')(dense_2) 38 | mu_output = Lambda(lambda x: x * self.action_bound)(out_mu) 39 | std_output = Dense(self.action_dim, activation='softplus')(dense_2) 40 | return tf.keras.models.Model(state_input, [mu_output, std_output]) 41 | 42 | def get_action(self, state): 43 | state = np.reshape(state, [1, self.state_dim]) 44 | mu, std = self.model.predict(state) 45 | mu, std = mu[0], std[0] 46 | return np.random.normal(mu, std, size=self.action_dim) 47 | 48 | def log_pdf(self, mu, std, action): 49 | std = tf.clip_by_value(std, self.std_bound[0], self.std_bound[1]) 50 | var = std ** 2 51 | log_policy_pdf = -0.5 * (action - mu) ** 2 / \ 52 | var - 0.5 * tf.math.log(var * 2 * np.pi) 53 | return tf.reduce_sum(log_policy_pdf, 1, keepdims=True) 54 | 55 | def compute_loss(self, mu, std, actions, advantages): 56 | log_policy_pdf = self.log_pdf(mu, std, actions) 57 | loss_policy = log_policy_pdf * advantages 58 | return tf.reduce_sum(-loss_policy) 59 | 60 | def train(self, states, actions, advantages): 61 | with tf.GradientTape() as tape: 62 | mu, std = self.model(states, training=True) 63 | loss = self.compute_loss(mu, std, actions, advantages) 64 | grads = tape.gradient(loss, self.model.trainable_variables) 65 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 66 | return loss 67 | 68 | 69 | class Critic: 70 | def __init__(self, state_dim): 71 | self.state_dim = state_dim 72 | self.model = self.create_model() 73 | self.opt = tf.keras.optimizers.Adam(args.critic_lr) 74 | 75 | def create_model(self): 76 | return tf.keras.Sequential([ 77 | Input((self.state_dim,)), 78 | Dense(32, activation='relu'), 79 | Dense(32, activation='relu'), 80 | Dense(16, activation='relu'), 81 | Dense(1, activation='linear') 82 | ]) 83 | 84 | def compute_loss(self, v_pred, td_targets): 85 | mse = tf.keras.losses.MeanSquaredError() 86 | return mse(td_targets, v_pred) 87 | 88 | def train(self, states, td_targets): 89 | with tf.GradientTape() as tape: 90 | v_pred = self.model(states, training=True) 91 | assert v_pred.shape == td_targets.shape 92 | loss = self.compute_loss(v_pred, tf.stop_gradient(td_targets)) 93 | grads = tape.gradient(loss, self.model.trainable_variables) 94 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 95 | return loss 96 | 97 | 98 | class Agent: 99 | def __init__(self, env_name): 100 | env = gym.make(env_name) 101 | self.env_name = env_name 102 | self.state_dim = env.observation_space.shape[0] 103 | self.action_dim = env.action_space.shape[0] 104 | self.action_bound = env.action_space.high[0] 105 | self.std_bound = [1e-2, 1.0] 106 | 107 | self.global_actor = Actor( 108 | self.state_dim, self.action_dim, self.action_bound, self.std_bound) 109 | self.global_critic = Critic(self.state_dim) 110 | self.num_workers = cpu_count() 111 | 112 | def train(self, max_episodes=1000): 113 | workers = [] 114 | 115 | for i in range(self.num_workers): 116 | env = gym.make(self.env_name) 117 | workers.append(WorkerAgent( 118 | env, self.global_actor, self.global_critic, max_episodes)) 119 | 120 | for worker in workers: 121 | worker.start() 122 | 123 | for worker in workers: 124 | worker.join() 125 | 126 | 127 | class WorkerAgent(Thread): 128 | def __init__(self, env, global_actor, global_critic, max_episodes): 129 | Thread.__init__(self) 130 | self.env = env 131 | self.state_dim = self.env.observation_space.shape[0] 132 | self.action_dim = self.env.action_space.shape[0] 133 | self.action_bound = self.env.action_space.high[0] 134 | self.std_bound = [1e-2, 1.0] 135 | 136 | self.max_episodes = max_episodes 137 | self.global_actor = global_actor 138 | self.global_critic = global_critic 139 | self.actor = Actor(self.state_dim, self.action_dim, 140 | self.action_bound, self.std_bound) 141 | self.critic = Critic(self.state_dim) 142 | 143 | self.actor.model.set_weights(self.global_actor.model.get_weights()) 144 | self.critic.model.set_weights(self.global_critic.model.get_weights()) 145 | 146 | def n_step_td_target(self, rewards, next_v_value, done): 147 | td_targets = np.zeros_like(rewards) 148 | cumulative = 0 149 | if not done: 150 | cumulative = next_v_value 151 | 152 | for k in reversed(range(0, len(rewards))): 153 | cumulative = args.gamma * cumulative + rewards[k] 154 | td_targets[k] = cumulative 155 | return td_targets 156 | 157 | def advatnage(self, td_targets, baselines): 158 | return td_targets - baselines 159 | 160 | def list_to_batch(self, list): 161 | batch = list[0] 162 | for elem in list[1:]: 163 | batch = np.append(batch, elem, axis=0) 164 | return batch 165 | 166 | def train(self): 167 | global CUR_EPISODE 168 | 169 | while self.max_episodes >= CUR_EPISODE: 170 | state_batch = [] 171 | action_batch = [] 172 | reward_batch = [] 173 | episode_reward, done = 0, False 174 | 175 | state = self.env.reset() 176 | 177 | while not done: 178 | # self.env.render() 179 | action = self.actor.get_action(state) 180 | action = np.clip(action, -self.action_bound, self.action_bound) 181 | 182 | next_state, reward, done, _ = self.env.step(action) 183 | 184 | state = np.reshape(state, [1, self.state_dim]) 185 | action = np.reshape(action, [1, 1]) 186 | next_state = np.reshape(next_state, [1, self.state_dim]) 187 | reward = np.reshape(reward, [1, 1]) 188 | 189 | state_batch.append(state) 190 | action_batch.append(action) 191 | reward_batch.append(reward) 192 | 193 | if len(state_batch) >= args.update_interval or done: 194 | states = self.list_to_batch(state_batch) 195 | actions = self.list_to_batch(action_batch) 196 | rewards = self.list_to_batch(reward_batch) 197 | 198 | next_v_value = self.critic.model.predict(next_state) 199 | td_targets = self.n_step_td_target( 200 | (rewards+8)/8, next_v_value, done) 201 | advantages = td_targets - self.critic.model.predict(states) 202 | 203 | actor_loss = self.global_actor.train( 204 | states, actions, advantages) 205 | critic_loss = self.global_critic.train( 206 | states, td_targets) 207 | 208 | self.actor.model.set_weights( 209 | self.global_actor.model.get_weights()) 210 | self.critic.model.set_weights( 211 | self.global_critic.model.get_weights()) 212 | 213 | state_batch = [] 214 | action_batch = [] 215 | reward_batch = [] 216 | td_target_batch = [] 217 | advatnage_batch = [] 218 | 219 | episode_reward += reward[0][0] 220 | state = next_state[0] 221 | 222 | print('EP{} EpisodeReward={}'.format(CUR_EPISODE, episode_reward)) 223 | wandb.log({'Reward': episode_reward}) 224 | CUR_EPISODE += 1 225 | 226 | def run(self): 227 | self.train() 228 | 229 | 230 | def main(): 231 | env_name = 'Pendulum-v0' 232 | agent = Agent(env_name) 233 | agent.train() 234 | 235 | 236 | if __name__ == "__main__": 237 | main() 238 | -------------------------------------------------------------------------------- /A3C/A3C_Discrete.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense 4 | 5 | import gym 6 | import argparse 7 | import numpy as np 8 | from threading import Thread, Lock 9 | from multiprocessing import cpu_count 10 | tf.keras.backend.set_floatx('float64') 11 | wandb.init(name='A3C', project="deep-rl-tf2") 12 | 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument('--gamma', type=float, default=0.99) 15 | parser.add_argument('--update_interval', type=int, default=5) 16 | parser.add_argument('--actor_lr', type=float, default=0.0005) 17 | parser.add_argument('--critic_lr', type=float, default=0.001) 18 | 19 | args = parser.parse_args() 20 | 21 | CUR_EPISODE = 0 22 | 23 | 24 | class Actor: 25 | def __init__(self, state_dim, action_dim): 26 | self.state_dim = state_dim 27 | self.action_dim = action_dim 28 | self.model = self.create_model() 29 | self.opt = tf.keras.optimizers.Adam(args.actor_lr) 30 | self.entropy_beta = 0.01 31 | 32 | def create_model(self): 33 | return tf.keras.Sequential([ 34 | Input((self.state_dim,)), 35 | Dense(32, activation='relu'), 36 | Dense(16, activation='relu'), 37 | Dense(self.action_dim, activation='softmax') 38 | ]) 39 | 40 | def compute_loss(self, actions, logits, advantages): 41 | ce_loss = tf.keras.losses.SparseCategoricalCrossentropy( 42 | from_logits=True) 43 | entropy_loss = tf.keras.losses.CategoricalCrossentropy( 44 | from_logits=True) 45 | actions = tf.cast(actions, tf.int32) 46 | policy_loss = ce_loss( 47 | actions, logits, sample_weight=tf.stop_gradient(advantages)) 48 | entropy = entropy_loss(logits, logits) 49 | return policy_loss - self.entropy_beta * entropy 50 | 51 | def train(self, states, actions, advantages): 52 | with tf.GradientTape() as tape: 53 | logits = self.model(states, training=True) 54 | loss = self.compute_loss( 55 | actions, logits, advantages) 56 | grads = tape.gradient(loss, self.model.trainable_variables) 57 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 58 | return loss 59 | 60 | 61 | class Critic: 62 | def __init__(self, state_dim): 63 | self.state_dim = state_dim 64 | self.model = self.create_model() 65 | self.opt = tf.keras.optimizers.Adam(args.critic_lr) 66 | 67 | def create_model(self): 68 | return tf.keras.Sequential([ 69 | Input((self.state_dim,)), 70 | Dense(32, activation='relu'), 71 | Dense(16, activation='relu'), 72 | Dense(16, activation='relu'), 73 | Dense(1, activation='linear') 74 | ]) 75 | 76 | def compute_loss(self, v_pred, td_targets): 77 | mse = tf.keras.losses.MeanSquaredError() 78 | return mse(td_targets, v_pred) 79 | 80 | def train(self, states, td_targets): 81 | with tf.GradientTape() as tape: 82 | v_pred = self.model(states, training=True) 83 | assert v_pred.shape == td_targets.shape 84 | loss = self.compute_loss(v_pred, tf.stop_gradient(td_targets)) 85 | grads = tape.gradient(loss, self.model.trainable_variables) 86 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 87 | return loss 88 | 89 | 90 | class Agent: 91 | def __init__(self, env_name): 92 | env = gym.make(env_name) 93 | self.env_name = env_name 94 | self.state_dim = env.observation_space.shape[0] 95 | self.action_dim = env.action_space.n 96 | 97 | self.global_actor = Actor(self.state_dim, self.action_dim) 98 | self.global_critic = Critic(self.state_dim) 99 | self.num_workers = cpu_count() 100 | 101 | def train(self, max_episodes=1000): 102 | workers = [] 103 | 104 | for i in range(self.num_workers): 105 | env = gym.make(self.env_name) 106 | workers.append(WorkerAgent( 107 | env, self.global_actor, self.global_critic, max_episodes)) 108 | 109 | for worker in workers: 110 | worker.start() 111 | 112 | for worker in workers: 113 | worker.join() 114 | 115 | 116 | class WorkerAgent(Thread): 117 | def __init__(self, env, global_actor, global_critic, max_episodes): 118 | Thread.__init__(self) 119 | self.lock = Lock() 120 | self.env = env 121 | self.state_dim = self.env.observation_space.shape[0] 122 | self.action_dim = self.env.action_space.n 123 | 124 | self.max_episodes = max_episodes 125 | self.global_actor = global_actor 126 | self.global_critic = global_critic 127 | self.actor = Actor(self.state_dim, self.action_dim) 128 | self.critic = Critic(self.state_dim) 129 | 130 | self.actor.model.set_weights(self.global_actor.model.get_weights()) 131 | self.critic.model.set_weights(self.global_critic.model.get_weights()) 132 | 133 | def n_step_td_target(self, rewards, next_v_value, done): 134 | td_targets = np.zeros_like(rewards) 135 | cumulative = 0 136 | if not done: 137 | cumulative = next_v_value 138 | 139 | for k in reversed(range(0, len(rewards))): 140 | cumulative = args.gamma * cumulative + rewards[k] 141 | td_targets[k] = cumulative 142 | return td_targets 143 | 144 | def advatnage(self, td_targets, baselines): 145 | return td_targets - baselines 146 | 147 | def list_to_batch(self, list): 148 | batch = list[0] 149 | for elem in list[1:]: 150 | batch = np.append(batch, elem, axis=0) 151 | return batch 152 | 153 | def train(self): 154 | global CUR_EPISODE 155 | 156 | while self.max_episodes >= CUR_EPISODE: 157 | state_batch = [] 158 | action_batch = [] 159 | reward_batch = [] 160 | episode_reward, done = 0, False 161 | 162 | state = self.env.reset() 163 | 164 | while not done: 165 | # self.env.render() 166 | probs = self.actor.model.predict( 167 | np.reshape(state, [1, self.state_dim])) 168 | action = np.random.choice(self.action_dim, p=probs[0]) 169 | 170 | next_state, reward, done, _ = self.env.step(action) 171 | 172 | state = np.reshape(state, [1, self.state_dim]) 173 | action = np.reshape(action, [1, 1]) 174 | next_state = np.reshape(next_state, [1, self.state_dim]) 175 | reward = np.reshape(reward, [1, 1]) 176 | 177 | state_batch.append(state) 178 | action_batch.append(action) 179 | reward_batch.append(reward) 180 | 181 | if len(state_batch) >= args.update_interval or done: 182 | states = self.list_to_batch(state_batch) 183 | actions = self.list_to_batch(action_batch) 184 | rewards = self.list_to_batch(reward_batch) 185 | 186 | next_v_value = self.critic.model.predict(next_state) 187 | td_targets = self.n_step_td_target( 188 | rewards, next_v_value, done) 189 | advantages = td_targets - self.critic.model.predict(states) 190 | 191 | with self.lock: 192 | actor_loss = self.global_actor.train( 193 | states, actions, advantages) 194 | critic_loss = self.global_critic.train( 195 | states, td_targets) 196 | 197 | self.actor.model.set_weights( 198 | self.global_actor.model.get_weights()) 199 | self.critic.model.set_weights( 200 | self.global_critic.model.get_weights()) 201 | 202 | state_batch = [] 203 | action_batch = [] 204 | reward_batch = [] 205 | td_target_batch = [] 206 | advatnage_batch = [] 207 | 208 | episode_reward += reward[0][0] 209 | state = next_state[0] 210 | 211 | print('EP{} EpisodeReward={}'.format(CUR_EPISODE, episode_reward)) 212 | wandb.log({'Reward': episode_reward}) 213 | CUR_EPISODE += 1 214 | 215 | def run(self): 216 | self.train() 217 | 218 | 219 | def main(): 220 | env_name = 'CartPole-v1' 221 | agent = Agent(env_name) 222 | agent.train() 223 | 224 | 225 | if __name__ == "__main__": 226 | main() 227 | -------------------------------------------------------------------------------- /DDPG/DDPG_Continuous.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense, Lambda, concatenate 4 | 5 | import gym 6 | import argparse 7 | import numpy as np 8 | import random 9 | from collections import deque 10 | 11 | tf.keras.backend.set_floatx('float64') 12 | wandb.init(name='DDPG', project="deep-rl-tf2") 13 | 14 | parser = argparse.ArgumentParser() 15 | parser.add_argument('--gamma', type=float, default=0.99) 16 | parser.add_argument('--actor_lr', type=float, default=0.0005) 17 | parser.add_argument('--critic_lr', type=float, default=0.001) 18 | parser.add_argument('--batch_size', type=int, default=64) 19 | parser.add_argument('--tau', type=float, default=0.05) 20 | parser.add_argument('--train_start', type=int, default=2000) 21 | 22 | args = parser.parse_args() 23 | 24 | class ReplayBuffer: 25 | def __init__(self, capacity=20000): 26 | self.buffer = deque(maxlen=capacity) 27 | 28 | def put(self, state, action, reward, next_state, done): 29 | self.buffer.append([state, action, reward, next_state, done]) 30 | 31 | def sample(self): 32 | sample = random.sample(self.buffer, args.batch_size) 33 | states, actions, rewards, next_states, done = map(np.asarray, zip(*sample)) 34 | states = np.array(states).reshape(args.batch_size, -1) 35 | next_states = np.array(next_states).reshape(args.batch_size, -1) 36 | return states, actions, rewards, next_states, done 37 | 38 | def size(self): 39 | return len(self.buffer) 40 | 41 | class Actor: 42 | def __init__(self, state_dim, action_dim, action_bound): 43 | self.state_dim = state_dim 44 | self.action_dim = action_dim 45 | self.action_bound = action_bound 46 | self.model = self.create_model() 47 | self.opt = tf.keras.optimizers.Adam(args.actor_lr) 48 | 49 | def create_model(self): 50 | return tf.keras.Sequential([ 51 | Input((self.state_dim,)), 52 | Dense(32, activation='relu'), 53 | Dense(32, activation='relu'), 54 | Dense(self.action_dim, activation='tanh'), 55 | Lambda(lambda x: x * self.action_bound) 56 | ]) 57 | 58 | def train(self, states, q_grads): 59 | with tf.GradientTape() as tape: 60 | grads = tape.gradient(self.model(states), self.model.trainable_variables, -q_grads) 61 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 62 | 63 | def predict(self, state): 64 | return self.model.predict(state) 65 | 66 | def get_action(self, state): 67 | state = np.reshape(state, [1, self.state_dim]) 68 | return self.model.predict(state)[0] 69 | 70 | 71 | 72 | class Critic: 73 | def __init__(self, state_dim, action_dim): 74 | self.state_dim = state_dim 75 | self.action_dim = action_dim 76 | self.model = self.create_model() 77 | self.opt = tf.keras.optimizers.Adam(args.critic_lr) 78 | 79 | def create_model(self): 80 | state_input = Input((self.state_dim,)) 81 | s1 = Dense(64, activation='relu')(state_input) 82 | s2 = Dense(32, activation='relu')(s1) 83 | action_input = Input((self.action_dim,)) 84 | a1 = Dense(32, activation='relu')(action_input) 85 | c1 = concatenate([s2, a1], axis=-1) 86 | c2 = Dense(16, activation='relu')(c1) 87 | output = Dense(1, activation='linear')(c2) 88 | return tf.keras.Model([state_input, action_input], output) 89 | 90 | def predict(self, inputs): 91 | return self.model.predict(inputs) 92 | 93 | def q_grads(self, states, actions): 94 | actions = tf.convert_to_tensor(actions) 95 | with tf.GradientTape() as tape: 96 | tape.watch(actions) 97 | q_values = self.model([states, actions]) 98 | q_values = tf.squeeze(q_values) 99 | return tape.gradient(q_values, actions) 100 | 101 | def compute_loss(self, v_pred, td_targets): 102 | mse = tf.keras.losses.MeanSquaredError() 103 | return mse(td_targets, v_pred) 104 | 105 | def train(self, states, actions, td_targets): 106 | with tf.GradientTape() as tape: 107 | v_pred = self.model([states, actions], training=True) 108 | assert v_pred.shape == td_targets.shape 109 | loss = self.compute_loss(v_pred, tf.stop_gradient(td_targets)) 110 | grads = tape.gradient(loss, self.model.trainable_variables) 111 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 112 | return loss 113 | 114 | 115 | class Agent: 116 | def __init__(self, env): 117 | self.env = env 118 | self.state_dim = self.env.observation_space.shape[0] 119 | self.action_dim = self.env.action_space.shape[0] 120 | self.action_bound = self.env.action_space.high[0] 121 | 122 | self.buffer = ReplayBuffer() 123 | 124 | self.actor = Actor(self.state_dim, self.action_dim, self.action_bound) 125 | self.critic = Critic(self.state_dim, self.action_dim) 126 | 127 | self.target_actor = Actor(self.state_dim, self.action_dim, self.action_bound) 128 | self.target_critic = Critic(self.state_dim, self.action_dim) 129 | 130 | actor_weights = self.actor.model.get_weights() 131 | critic_weights = self.critic.model.get_weights() 132 | self.target_actor.model.set_weights(actor_weights) 133 | self.target_critic.model.set_weights(critic_weights) 134 | 135 | 136 | def target_update(self): 137 | actor_weights = self.actor.model.get_weights() 138 | t_actor_weights = self.target_actor.model.get_weights() 139 | critic_weights = self.critic.model.get_weights() 140 | t_critic_weights = self.target_critic.model.get_weights() 141 | 142 | for i in range(len(actor_weights)): 143 | t_actor_weights[i] = args.tau * actor_weights[i] + (1 - args.tau) * t_actor_weights[i] 144 | 145 | for i in range(len(critic_weights)): 146 | t_critic_weights[i] = args.tau * critic_weights[i] + (1 - args.tau) * t_critic_weights[i] 147 | 148 | self.target_actor.model.set_weights(t_actor_weights) 149 | self.target_critic.model.set_weights(t_critic_weights) 150 | 151 | 152 | def td_target(self, rewards, q_values, dones): 153 | targets = np.asarray(q_values) 154 | for i in range(q_values.shape[0]): 155 | if dones[i]: 156 | targets[i] = rewards[i] 157 | else: 158 | targets[i] = args.gamma * q_values[i] 159 | return targets 160 | 161 | def list_to_batch(self, list): 162 | batch = list[0] 163 | for elem in list[1:]: 164 | batch = np.append(batch, elem, axis=0) 165 | return batch 166 | 167 | def ou_noise(self, x, rho=0.15, mu=0, dt=1e-1, sigma=0.2, dim=1): 168 | return x + rho * (mu-x) * dt + sigma * np.sqrt(dt) * np.random.normal(size=dim) 169 | 170 | def replay(self): 171 | for _ in range(10): 172 | states, actions, rewards, next_states, dones = self.buffer.sample() 173 | target_q_values = self.target_critic.predict([next_states, self.target_actor.predict(next_states)]) 174 | td_targets = self.td_target(rewards, target_q_values, dones) 175 | 176 | self.critic.train(states, actions, td_targets) 177 | 178 | s_actions = self.actor.predict(states) 179 | s_grads = self.critic.q_grads(states, s_actions) 180 | grads = np.array(s_grads).reshape((-1, self.action_dim)) 181 | self.actor.train(states, grads) 182 | self.target_update() 183 | 184 | def train(self, max_episodes=1000): 185 | for ep in range(max_episodes): 186 | episode_reward, done = 0, False 187 | 188 | state = self.env.reset() 189 | bg_noise = np.zeros(self.action_dim) 190 | while not done: 191 | # self.env.render() 192 | action = self.actor.get_action(state) 193 | noise = self.ou_noise(bg_noise, dim=self.action_dim) 194 | action = np.clip(action + noise, -self.action_bound, self.action_bound) 195 | 196 | next_state, reward, done, _ = self.env.step(action) 197 | self.buffer.put(state, action, (reward+8)/8, next_state, done) 198 | bg_noise = noise 199 | episode_reward += reward 200 | state = next_state 201 | if self.buffer.size() >= args.batch_size and self.buffer.size() >= args.train_start: 202 | self.replay() 203 | print('EP{} EpisodeReward={}'.format(ep, episode_reward)) 204 | wandb.log({'Reward': episode_reward}) 205 | 206 | 207 | def main(): 208 | env_name = 'Pendulum-v0' 209 | env = gym.make(env_name) 210 | agent = Agent(env) 211 | agent.train() 212 | 213 | 214 | if __name__ == "__main__": 215 | main() 216 | -------------------------------------------------------------------------------- /DQN/DQN_Discrete.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense 4 | from tensorflow.keras.optimizers import Adam 5 | 6 | import gym 7 | import argparse 8 | import numpy as np 9 | from collections import deque 10 | import random 11 | 12 | tf.keras.backend.set_floatx('float64') 13 | wandb.init(name='DQN', project="deep-rl-tf2") 14 | 15 | parser = argparse.ArgumentParser() 16 | parser.add_argument('--gamma', type=float, default=0.95) 17 | parser.add_argument('--lr', type=float, default=0.005) 18 | parser.add_argument('--batch_size', type=int, default=32) 19 | parser.add_argument('--eps', type=float, default=1.0) 20 | parser.add_argument('--eps_decay', type=float, default=0.995) 21 | parser.add_argument('--eps_min', type=float, default=0.01) 22 | 23 | args = parser.parse_args() 24 | 25 | class ReplayBuffer: 26 | def __init__(self, capacity=10000): 27 | self.buffer = deque(maxlen=capacity) 28 | 29 | def put(self, state, action, reward, next_state, done): 30 | self.buffer.append([state, action, reward, next_state, done]) 31 | 32 | def sample(self): 33 | sample = random.sample(self.buffer, args.batch_size) 34 | states, actions, rewards, next_states, done = map(np.asarray, zip(*sample)) 35 | states = np.array(states).reshape(args.batch_size, -1) 36 | next_states = np.array(next_states).reshape(args.batch_size, -1) 37 | return states, actions, rewards, next_states, done 38 | 39 | def size(self): 40 | return len(self.buffer) 41 | 42 | class ActionStateModel: 43 | def __init__(self, state_dim, aciton_dim): 44 | self.state_dim = state_dim 45 | self.action_dim = aciton_dim 46 | self.epsilon = args.eps 47 | 48 | self.model = self.create_model() 49 | 50 | def create_model(self): 51 | model = tf.keras.Sequential([ 52 | Input((self.state_dim,)), 53 | Dense(32, activation='relu'), 54 | Dense(16, activation='relu'), 55 | Dense(self.action_dim) 56 | ]) 57 | model.compile(loss='mse', optimizer=Adam(args.lr)) 58 | return model 59 | 60 | def predict(self, state): 61 | return self.model.predict(state) 62 | 63 | def get_action(self, state): 64 | state = np.reshape(state, [1, self.state_dim]) 65 | self.epsilon *= args.eps_decay 66 | self.epsilon = max(self.epsilon, args.eps_min) 67 | q_value = self.predict(state)[0] 68 | if np.random.random() < self.epsilon: 69 | return random.randint(0, self.action_dim-1) 70 | return np.argmax(q_value) 71 | 72 | def train(self, states, targets): 73 | self.model.fit(states, targets, epochs=1, verbose=0) 74 | 75 | 76 | class Agent: 77 | def __init__(self, env): 78 | self.env = env 79 | self.state_dim = self.env.observation_space.shape[0] 80 | self.action_dim = self.env.action_space.n 81 | 82 | self.model = ActionStateModel(self.state_dim, self.action_dim) 83 | self.target_model = ActionStateModel(self.state_dim, self.action_dim) 84 | self.target_update() 85 | 86 | self.buffer = ReplayBuffer() 87 | 88 | def target_update(self): 89 | weights = self.model.model.get_weights() 90 | self.target_model.model.set_weights(weights) 91 | 92 | def replay(self): 93 | for _ in range(10): 94 | states, actions, rewards, next_states, done = self.buffer.sample() 95 | targets = self.target_model.predict(states) 96 | next_q_values = self.target_model.predict(next_states).max(axis=1) 97 | targets[range(args.batch_size), actions] = rewards + (1-done) * next_q_values * args.gamma 98 | self.model.train(states, targets) 99 | 100 | def train(self, max_episodes=1000): 101 | for ep in range(max_episodes): 102 | done, total_reward = False, 0 103 | state = self.env.reset() 104 | while not done: 105 | action = self.model.get_action(state) 106 | next_state, reward, done, _ = self.env.step(action) 107 | self.buffer.put(state, action, reward*0.01, next_state, done) 108 | total_reward += reward 109 | state = next_state 110 | if self.buffer.size() >= args.batch_size: 111 | self.replay() 112 | self.target_update() 113 | print('EP{} EpisodeReward={}'.format(ep, total_reward)) 114 | wandb.log({'Reward': total_reward}) 115 | 116 | 117 | def main(): 118 | env = gym.make('CartPole-v1') 119 | agent = Agent(env) 120 | agent.train(max_episodes=1000) 121 | 122 | if __name__ == "__main__": 123 | main() 124 | -------------------------------------------------------------------------------- /DRQN/DRQN_Discrete.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense, LSTM 4 | from tensorflow.keras.optimizers import Adam 5 | 6 | import gym 7 | import argparse 8 | import numpy as np 9 | from collections import deque 10 | import random 11 | 12 | tf.keras.backend.set_floatx('float64') 13 | wandb.init(name='DRQN', project="deep-rl-tf2") 14 | 15 | parser = argparse.ArgumentParser() 16 | parser.add_argument('--gamma', type=float, default=0.95) 17 | parser.add_argument('--lr', type=float, default=0.005) 18 | parser.add_argument('--batch_size', type=int, default=32) 19 | parser.add_argument('--time_steps', type=int, default=4) 20 | parser.add_argument('--eps', type=float, default=1.0) 21 | parser.add_argument('--eps_decay', type=float, default=0.995) 22 | parser.add_argument('--eps_min', type=float, default=0.01) 23 | 24 | args = parser.parse_args() 25 | 26 | class ReplayBuffer: 27 | def __init__(self, capacity=10000): 28 | self.buffer = deque(maxlen=capacity) 29 | 30 | def put(self, state, action, reward, next_state, done): 31 | self.buffer.append([state, action, reward, next_state, done]) 32 | 33 | def sample(self): 34 | sample = random.sample(self.buffer, args.batch_size) 35 | states, actions, rewards, next_states, done = map(np.asarray, zip(*sample)) 36 | states = np.array(states).reshape(args.batch_size, args.time_steps, -1) 37 | next_states = np.array(next_states).reshape(args.batch_size, args.time_steps, -1) 38 | return states, actions, rewards, next_states, done 39 | 40 | def size(self): 41 | return len(self.buffer) 42 | 43 | class ActionStateModel: 44 | def __init__(self, state_dim, aciton_dim): 45 | self.state_dim = state_dim 46 | self.action_dim = aciton_dim 47 | self.epsilon = args.eps 48 | 49 | self.opt = Adam(args.lr) 50 | self.compute_loss = tf.keras.losses.MeanSquaredError() 51 | self.model = self.create_model() 52 | 53 | def create_model(self): 54 | return tf.keras.Sequential([ 55 | Input((args.time_steps, self.state_dim)), 56 | LSTM(32, activation='tanh'), 57 | Dense(16, activation='relu'), 58 | Dense(self.action_dim) 59 | ]) 60 | 61 | def predict(self, state): 62 | return self.model.predict(state) 63 | 64 | def get_action(self, state): 65 | state = np.reshape(state, [1, args.time_steps, self.state_dim]) 66 | self.epsilon *= args.eps_decay 67 | self.epsilon = max(self.epsilon, args.eps_min) 68 | q_value = self.predict(state)[0] 69 | if np.random.random() < self.epsilon: 70 | return random.randint(0, self.action_dim-1) 71 | return np.argmax(q_value) 72 | 73 | def train(self, states, targets): 74 | targets = tf.stop_gradient(targets) 75 | with tf.GradientTape() as tape: 76 | logits = self.model(states, training=True) 77 | assert targets.shape == logits.shape 78 | loss = self.compute_loss(targets, logits) 79 | grads = tape.gradient(loss, self.model.trainable_variables) 80 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 81 | 82 | 83 | 84 | class Agent: 85 | def __init__(self, env): 86 | self.env = env 87 | self.state_dim = self.env.observation_space.shape[0] 88 | self.action_dim = self.env.action_space.n 89 | 90 | self.states = np.zeros([args.time_steps, self.state_dim]) 91 | 92 | self.model = ActionStateModel(self.state_dim, self.action_dim) 93 | self.target_model = ActionStateModel(self.state_dim, self.action_dim) 94 | self.target_update() 95 | 96 | self.buffer = ReplayBuffer() 97 | 98 | def target_update(self): 99 | weights = self.model.model.get_weights() 100 | self.target_model.model.set_weights(weights) 101 | 102 | def replay(self): 103 | for _ in range(10): 104 | states, actions, rewards, next_states, done = self.buffer.sample() 105 | targets = self.target_model.predict(states) 106 | next_q_values = self.target_model.predict(next_states).max(axis=1) 107 | targets[range(args.batch_size), actions] = rewards + (1-done) * next_q_values * args.gamma 108 | self.model.train(states, targets) 109 | 110 | def update_states(self, next_state): 111 | self.states = np.roll(self.states, -1, axis=0) 112 | self.states[-1] = next_state 113 | 114 | def train(self, max_episodes=1000): 115 | for ep in range(max_episodes): 116 | done, total_reward = False, 0 117 | self.states = np.zeros([args.time_steps, self.state_dim]) 118 | self.update_states(self.env.reset()) 119 | while not done: 120 | action = self.model.get_action(self.states) 121 | next_state, reward, done, _ = self.env.step(action) 122 | prev_states = self.states 123 | self.update_states(next_state) 124 | self.buffer.put(prev_states, action, reward*0.01, self.states, done) 125 | total_reward += reward 126 | 127 | if self.buffer.size() >= args.batch_size: 128 | self.replay() 129 | self.target_update() 130 | print('EP{} EpisodeReward={}'.format(ep, total_reward)) 131 | wandb.log({'Reward': total_reward}) 132 | 133 | 134 | def main(): 135 | env = gym.make('CartPole-v1') 136 | agent = Agent(env) 137 | agent.train(max_episodes=1000) 138 | 139 | if __name__ == "__main__": 140 | main() 141 | -------------------------------------------------------------------------------- /DoubleDQN/DoubleDQN_Discrete.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense, Flatten, Lambda 4 | from tensorflow.keras.optimizers import Adam 5 | 6 | import gym 7 | import argparse 8 | import numpy as np 9 | from collections import deque 10 | import random 11 | 12 | tf.keras.backend.set_floatx('float64') 13 | wandb.init(name='DoubleDQN', project="deep-rl-tf2") 14 | 15 | parser = argparse.ArgumentParser() 16 | parser.add_argument('--gamma', type=float, default=0.95) 17 | parser.add_argument('--lr', type=float, default=0.005) 18 | parser.add_argument('--batch_size', type=int, default=32) 19 | parser.add_argument('--eps', type=float, default=1.0) 20 | parser.add_argument('--eps_decay', type=float, default=0.995) 21 | parser.add_argument('--eps_min', type=float, default=0.01) 22 | 23 | args = parser.parse_args() 24 | 25 | class ReplayBuffer: 26 | def __init__(self, capacity=10000): 27 | self.buffer = deque(maxlen=capacity) 28 | 29 | def put(self, state, action, reward, next_state, done): 30 | self.buffer.append([state, action, reward, next_state, done]) 31 | 32 | def sample(self): 33 | sample = random.sample(self.buffer, args.batch_size) 34 | states, actions, rewards, next_states, done = map(np.asarray, zip(*sample)) 35 | states = np.array(states).reshape(args.batch_size, -1) 36 | next_states = np.array(next_states).reshape(args.batch_size, -1) 37 | return states, actions, rewards, next_states, done 38 | 39 | def size(self): 40 | return len(self.buffer) 41 | 42 | class ActionStateModel: 43 | def __init__(self, state_dim, aciton_dim): 44 | self.state_dim = state_dim 45 | self.action_dim = aciton_dim 46 | self.epsilon = args.eps 47 | 48 | self.model = self.create_model() 49 | 50 | def create_model(self): 51 | model = tf.keras.Sequential([ 52 | Input((self.state_dim,)), 53 | Dense(32, activation='relu'), 54 | Dense(16, activation='relu'), 55 | Dense(self.action_dim) 56 | ]) 57 | model.compile(loss='mse', optimizer=Adam(args.lr)) 58 | return model 59 | 60 | def predict(self, state): 61 | return self.model.predict(state) 62 | 63 | def get_action(self, state): 64 | state = np.reshape(state, [1, self.state_dim]) 65 | self.epsilon *= args.eps_decay 66 | self.epsilon = max(self.epsilon, args.eps_min) 67 | q_value = self.predict(state)[0] 68 | if np.random.random() < self.epsilon: 69 | return random.randint(0, self.action_dim-1) 70 | return np.argmax(q_value) 71 | 72 | def train(self, states, targets): 73 | self.model.fit(states, targets, epochs=1, verbose=0) 74 | 75 | 76 | class Agent: 77 | def __init__(self, env): 78 | self.env = env 79 | self.state_dim = self.env.observation_space.shape[0] 80 | self.action_dim = self.env.action_space.n 81 | 82 | self.model = ActionStateModel(self.state_dim, self.action_dim) 83 | self.target_model = ActionStateModel(self.state_dim, self.action_dim) 84 | self.target_update() 85 | 86 | self.buffer = ReplayBuffer() 87 | 88 | def target_update(self): 89 | weights = self.model.model.get_weights() 90 | self.target_model.model.set_weights(weights) 91 | 92 | def replay(self): 93 | for _ in range(10): 94 | states, actions, rewards, next_states, done = self.buffer.sample() 95 | targets = self.target_model.predict(states) 96 | next_q_values = self.target_model.predict(next_states)[range(args.batch_size),np.argmax(self.model.predict(next_states), axis=1)] 97 | targets[range(args.batch_size), actions] = rewards + (1-done) * next_q_values * args.gamma 98 | self.model.train(states, targets) 99 | 100 | def train(self, max_episodes=1000): 101 | for ep in range(max_episodes): 102 | done, total_reward = False, 0 103 | state = self.env.reset() 104 | while not done: 105 | action = self.model.get_action(state) 106 | next_state, reward, done, _ = self.env.step(action) 107 | self.buffer.put(state, action, reward*0.01, next_state, done) 108 | total_reward += reward 109 | state = next_state 110 | 111 | if self.buffer.size() >= args.batch_size: 112 | self.replay() 113 | self.target_update() 114 | print('EP{} EpisodeReward={}'.format(ep, total_reward)) 115 | wandb.log({'Reward': total_reward}) 116 | 117 | 118 | def main(): 119 | env = gym.make('CartPole-v1') 120 | agent = Agent(env) 121 | agent.train(max_episodes=1000) 122 | 123 | if __name__ == "__main__": 124 | main() 125 | -------------------------------------------------------------------------------- /DuelingDQN/DuelingDQN_Discrete.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense, Add 4 | from tensorflow.keras.optimizers import Adam 5 | 6 | import gym 7 | import argparse 8 | import numpy as np 9 | from collections import deque 10 | import random 11 | 12 | tf.keras.backend.set_floatx('float64') 13 | wandb.init(name='DuelingDQN', project="deep-rl-tf2") 14 | 15 | parser = argparse.ArgumentParser() 16 | parser.add_argument('--gamma', type=float, default=0.95) 17 | parser.add_argument('--lr', type=float, default=0.005) 18 | parser.add_argument('--batch_size', type=int, default=32) 19 | parser.add_argument('--eps', type=float, default=1.0) 20 | parser.add_argument('--eps_decay', type=float, default=0.995) 21 | parser.add_argument('--eps_min', type=float, default=0.01) 22 | 23 | args = parser.parse_args() 24 | 25 | class ReplayBuffer: 26 | def __init__(self, capacity=10000): 27 | self.buffer = deque(maxlen=capacity) 28 | 29 | def put(self, state, action, reward, next_state, done): 30 | self.buffer.append([state, action, reward, next_state, done]) 31 | 32 | def sample(self): 33 | sample = random.sample(self.buffer, args.batch_size) 34 | states, actions, rewards, next_states, done = map(np.asarray, zip(*sample)) 35 | states = np.array(states).reshape(args.batch_size, -1) 36 | next_states = np.array(next_states).reshape(args.batch_size, -1) 37 | return states, actions, rewards, next_states, done 38 | 39 | def size(self): 40 | return len(self.buffer) 41 | 42 | class ActionStateModel: 43 | def __init__(self, state_dim, aciton_dim): 44 | self.state_dim = state_dim 45 | self.action_dim = aciton_dim 46 | self.epsilon = args.eps 47 | 48 | self.model = self.create_model() 49 | 50 | def create_model(self): 51 | backbone = tf.keras.Sequential([ 52 | Input((self.state_dim,)), 53 | Dense(32, activation='relu'), 54 | Dense(16, activation='relu') 55 | ]) 56 | state_input = Input((self.state_dim,)) 57 | backbone_1 = Dense(32, activation='relu')(state_input) 58 | backbone_2 = Dense(16, activation='relu')(backbone_1) 59 | value_output = Dense(1)(backbone_2) 60 | advantage_output = Dense(self.action_dim)(backbone_2) 61 | output = Add()([value_output, advantage_output]) 62 | model = tf.keras.Model(state_input, output) 63 | model.compile(loss='mse', optimizer=Adam(args.lr)) 64 | return model 65 | 66 | def predict(self, state): 67 | return self.model.predict(state) 68 | 69 | def get_action(self, state): 70 | state = np.reshape(state, [1, self.state_dim]) 71 | self.epsilon *= args.eps_decay 72 | self.epsilon = max(self.epsilon, args.eps_min) 73 | q_value = self.predict(state)[0] 74 | if np.random.random() < self.epsilon: 75 | return random.randint(0, self.action_dim-1) 76 | return np.argmax(q_value) 77 | 78 | def train(self, states, targets): 79 | self.model.fit(states, targets, epochs=1, verbose=0) 80 | 81 | 82 | class Agent: 83 | def __init__(self, env): 84 | self.env = env 85 | self.state_dim = self.env.observation_space.shape[0] 86 | self.action_dim = self.env.action_space.n 87 | 88 | self.model = ActionStateModel(self.state_dim, self.action_dim) 89 | self.target_model = ActionStateModel(self.state_dim, self.action_dim) 90 | self.target_update() 91 | 92 | self.buffer = ReplayBuffer() 93 | 94 | def target_update(self): 95 | weights = self.model.model.get_weights() 96 | self.target_model.model.set_weights(weights) 97 | 98 | def replay(self): 99 | for _ in range(10): 100 | states, actions, rewards, next_states, done = self.buffer.sample() 101 | targets = self.target_model.predict(states) 102 | next_q_values = self.target_model.predict(next_states).max(axis=1) 103 | targets[range(args.batch_size), actions] = rewards + (1-done) * next_q_values * args.gamma 104 | self.model.train(states, targets) 105 | 106 | def train(self, max_episodes=1000): 107 | for ep in range(max_episodes): 108 | done, total_reward = False, 0 109 | state = self.env.reset() 110 | while not done: 111 | action = self.model.get_action(state) 112 | next_state, reward, done, _ = self.env.step(action) 113 | self.buffer.put(state, action, reward*0.01, next_state, done) 114 | total_reward += reward 115 | state = next_state 116 | 117 | if self.buffer.size() >= args.batch_size: 118 | self.replay() 119 | self.target_update() 120 | print('EP{} EpisodeReward={}'.format(ep, total_reward)) 121 | wandb.log({'Reward': total_reward}) 122 | 123 | 124 | def main(): 125 | env = gym.make('CartPole-v1') 126 | agent = Agent(env) 127 | agent.train(max_episodes=1000) 128 | 129 | if __name__ == "__main__": 130 | main() 131 | -------------------------------------------------------------------------------- /DuelingDoubleDQN/DuelingDoubleDQN_Discrete.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense, Add 4 | from tensorflow.keras.optimizers import Adam 5 | 6 | import gym 7 | import argparse 8 | import numpy as np 9 | from collections import deque 10 | import random 11 | 12 | tf.keras.backend.set_floatx('float64') 13 | wandb.init(name='DuelingDoubleDQN', project="deep-rl-tf2") 14 | 15 | parser = argparse.ArgumentParser() 16 | parser.add_argument('--gamma', type=float, default=0.95) 17 | parser.add_argument('--lr', type=float, default=0.005) 18 | parser.add_argument('--batch_size', type=int, default=32) 19 | parser.add_argument('--eps', type=float, default=1.0) 20 | parser.add_argument('--eps_decay', type=float, default=0.995) 21 | parser.add_argument('--eps_min', type=float, default=0.01) 22 | 23 | args = parser.parse_args() 24 | 25 | class ReplayBuffer: 26 | def __init__(self, capacity=10000): 27 | self.buffer = deque(maxlen=capacity) 28 | 29 | def put(self, state, action, reward, next_state, done): 30 | self.buffer.append([state, action, reward, next_state, done]) 31 | 32 | def sample(self): 33 | sample = random.sample(self.buffer, args.batch_size) 34 | states, actions, rewards, next_states, done = map(np.asarray, zip(*sample)) 35 | states = np.array(states).reshape(args.batch_size, -1) 36 | next_states = np.array(next_states).reshape(args.batch_size, -1) 37 | return states, actions, rewards, next_states, done 38 | 39 | def size(self): 40 | return len(self.buffer) 41 | 42 | class ActionStateModel: 43 | def __init__(self, state_dim, aciton_dim): 44 | self.state_dim = state_dim 45 | self.action_dim = aciton_dim 46 | self.epsilon = args.eps 47 | 48 | self.model = self.create_model() 49 | 50 | def create_model(self): 51 | backbone = tf.keras.Sequential([ 52 | Input((self.state_dim,)), 53 | Dense(32, activation='relu'), 54 | Dense(16, activation='relu') 55 | ]) 56 | state_input = Input((self.state_dim,)) 57 | backbone_1 = Dense(32, activation='relu')(state_input) 58 | backbone_2 = Dense(16, activation='relu')(backbone_1) 59 | value_output = Dense(1)(backbone_2) 60 | advantage_output = Dense(self.action_dim)(backbone_2) 61 | output = Add()([value_output, advantage_output]) 62 | model = tf.keras.Model(state_input, output) 63 | model.compile(loss='mse', optimizer=Adam(args.lr)) 64 | return model 65 | 66 | def predict(self, state): 67 | return self.model.predict(state) 68 | 69 | def get_action(self, state): 70 | state = np.reshape(state, [1, self.state_dim]) 71 | self.epsilon *= args.eps_decay 72 | self.epsilon = max(self.epsilon, args.eps_min) 73 | q_value = self.predict(state)[0] 74 | if np.random.random() < self.epsilon: 75 | return random.randint(0, self.action_dim-1) 76 | return np.argmax(q_value) 77 | 78 | def train(self, states, targets): 79 | self.model.fit(states, targets, epochs=1, verbose=0) 80 | 81 | 82 | class Agent: 83 | def __init__(self, env): 84 | self.env = env 85 | self.state_dim = self.env.observation_space.shape[0] 86 | self.action_dim = self.env.action_space.n 87 | 88 | self.model = ActionStateModel(self.state_dim, self.action_dim) 89 | self.target_model = ActionStateModel(self.state_dim, self.action_dim) 90 | self.target_update() 91 | 92 | self.buffer = ReplayBuffer() 93 | 94 | def target_update(self): 95 | weights = self.model.model.get_weights() 96 | self.target_model.model.set_weights(weights) 97 | 98 | def replay(self): 99 | for _ in range(10): 100 | states, actions, rewards, next_states, done = self.buffer.sample() 101 | targets = self.target_model.predict(states) 102 | next_q_values = self.target_model.predict(next_states)[range(args.batch_size),np.argmax(self.model.predict(next_states), axis=1)] 103 | targets[range(args.batch_size), actions] = rewards + (1-done) * next_q_values * args.gamma 104 | self.model.train(states, targets) 105 | 106 | def train(self, max_episodes=1000): 107 | for ep in range(max_episodes): 108 | done, total_reward = False, 0 109 | state = self.env.reset() 110 | while not done: 111 | action = self.model.get_action(state) 112 | next_state, reward, done, _ = self.env.step(action) 113 | self.buffer.put(state, action, reward*0.01, next_state, done) 114 | total_reward += reward 115 | state = next_state 116 | 117 | if self.buffer.size() >= args.batch_size: 118 | self.replay() 119 | self.target_update() 120 | print('EP{} EpisodeReward={}'.format(ep, total_reward)) 121 | wandb.log({'Reward': total_reward}) 122 | 123 | 124 | def main(): 125 | env = gym.make('CartPole-v1') 126 | agent = Agent(env) 127 | agent.train(max_episodes=1000) 128 | 129 | if __name__ == "__main__": 130 | main() 131 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /PPO/PPO_Continuous.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense, Lambda 4 | 5 | import gym 6 | import argparse 7 | import numpy as np 8 | 9 | tf.keras.backend.set_floatx('float64') 10 | 11 | wandb.init(name='PPO', project="deep-rl-tf2") 12 | 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument('--gamma', type=float, default=0.99) 15 | parser.add_argument('--update_interval', type=int, default=5) 16 | parser.add_argument('--actor_lr', type=float, default=0.0005) 17 | parser.add_argument('--critic_lr', type=float, default=0.001) 18 | parser.add_argument('--clip_ratio', type=float, default=0.1) 19 | parser.add_argument('--lmbda', type=float, default=0.95) 20 | parser.add_argument('--epochs', type=int, default=3) 21 | 22 | args = parser.parse_args() 23 | 24 | 25 | class Actor: 26 | def __init__(self, state_dim, action_dim, action_bound, std_bound): 27 | self.state_dim = state_dim 28 | self.action_dim = action_dim 29 | self.action_bound = action_bound 30 | self.std_bound = std_bound 31 | self.model = self.create_model() 32 | self.opt = tf.keras.optimizers.Adam(args.actor_lr) 33 | 34 | def get_action(self, state): 35 | state = np.reshape(state, [1, self.state_dim]) 36 | mu, std = self.model.predict(state) 37 | action = np.random.normal(mu[0], std[0], size=self.action_dim) 38 | action = np.clip(action, -self.action_bound, self.action_bound) 39 | log_policy = self.log_pdf(mu, std, action) 40 | 41 | return log_policy, action 42 | 43 | def log_pdf(self, mu, std, action): 44 | std = tf.clip_by_value(std, self.std_bound[0], self.std_bound[1]) 45 | var = std ** 2 46 | log_policy_pdf = -0.5 * (action - mu) ** 2 / \ 47 | var - 0.5 * tf.math.log(var * 2 * np.pi) 48 | return tf.reduce_sum(log_policy_pdf, 1, keepdims=True) 49 | 50 | def create_model(self): 51 | state_input = Input((self.state_dim,)) 52 | dense_1 = Dense(32, activation='relu')(state_input) 53 | dense_2 = Dense(32, activation='relu')(dense_1) 54 | out_mu = Dense(self.action_dim, activation='tanh')(dense_2) 55 | mu_output = Lambda(lambda x: x * self.action_bound)(out_mu) 56 | std_output = Dense(self.action_dim, activation='softplus')(dense_2) 57 | return tf.keras.models.Model(state_input, [mu_output, std_output]) 58 | 59 | def compute_loss(self, log_old_policy, log_new_policy, actions, gaes): 60 | ratio = tf.exp(log_new_policy - tf.stop_gradient(log_old_policy)) 61 | gaes = tf.stop_gradient(gaes) 62 | clipped_ratio = tf.clip_by_value( 63 | ratio, 1.0-args.clip_ratio, 1.0+args.clip_ratio) 64 | surrogate = -tf.minimum(ratio * gaes, clipped_ratio * gaes) 65 | return tf.reduce_mean(surrogate) 66 | 67 | def train(self, log_old_policy, states, actions, gaes): 68 | with tf.GradientTape() as tape: 69 | mu, std = self.model(states, training=True) 70 | log_new_policy = self.log_pdf(mu, std, actions) 71 | loss = self.compute_loss( 72 | log_old_policy, log_new_policy, actions, gaes) 73 | grads = tape.gradient(loss, self.model.trainable_variables) 74 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 75 | return loss 76 | 77 | 78 | class Critic: 79 | def __init__(self, state_dim): 80 | self.state_dim = state_dim 81 | self.model = self.create_model() 82 | self.opt = tf.keras.optimizers.Adam(args.critic_lr) 83 | 84 | def create_model(self): 85 | return tf.keras.Sequential([ 86 | Input((self.state_dim,)), 87 | Dense(32, activation='relu'), 88 | Dense(32, activation='relu'), 89 | Dense(16, activation='relu'), 90 | Dense(1, activation='linear') 91 | ]) 92 | 93 | def compute_loss(self, v_pred, td_targets): 94 | mse = tf.keras.losses.MeanSquaredError() 95 | return mse(td_targets, v_pred) 96 | 97 | def train(self, states, td_targets): 98 | with tf.GradientTape() as tape: 99 | v_pred = self.model(states, training=True) 100 | assert v_pred.shape == td_targets.shape 101 | loss = self.compute_loss(v_pred, tf.stop_gradient(td_targets)) 102 | grads = tape.gradient(loss, self.model.trainable_variables) 103 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 104 | return loss 105 | 106 | 107 | class Agent: 108 | def __init__(self, env): 109 | self.env = env 110 | self.state_dim = self.env.observation_space.shape[0] 111 | self.action_dim = self.env.action_space.shape[0] 112 | self.action_bound = self.env.action_space.high[0] 113 | self.std_bound = [1e-2, 1.0] 114 | 115 | self.actor_opt = tf.keras.optimizers.Adam(args.actor_lr) 116 | self.critic_opt = tf.keras.optimizers.Adam(args.critic_lr) 117 | self.actor = Actor(self.state_dim, self.action_dim, 118 | self.action_bound, self.std_bound) 119 | self.critic = Critic(self.state_dim) 120 | 121 | def gae_target(self, rewards, v_values, next_v_value, done): 122 | n_step_targets = np.zeros_like(rewards) 123 | gae = np.zeros_like(rewards) 124 | gae_cumulative = 0 125 | forward_val = 0 126 | 127 | if not done: 128 | forward_val = next_v_value 129 | 130 | for k in reversed(range(0, len(rewards))): 131 | delta = rewards[k] + args.gamma * forward_val - v_values[k] 132 | gae_cumulative = args.gamma * args.lmbda * gae_cumulative + delta 133 | gae[k] = gae_cumulative 134 | forward_val = v_values[k] 135 | n_step_targets[k] = gae[k] + v_values[k] 136 | return gae, n_step_targets 137 | 138 | def list_to_batch(self, list): 139 | batch = list[0] 140 | for elem in list[1:]: 141 | batch = np.append(batch, elem, axis=0) 142 | return batch 143 | 144 | def train(self, max_episodes=1000): 145 | for ep in range(max_episodes): 146 | state_batch = [] 147 | action_batch = [] 148 | reward_batch = [] 149 | old_policy_batch = [] 150 | 151 | episode_reward, done = 0, False 152 | 153 | state = self.env.reset() 154 | 155 | while not done: 156 | # self.env.render() 157 | log_old_policy, action = self.actor.get_action(state) 158 | 159 | next_state, reward, done, _ = self.env.step(action) 160 | 161 | state = np.reshape(state, [1, self.state_dim]) 162 | action = np.reshape(action, [1, 1]) 163 | next_state = np.reshape(next_state, [1, self.state_dim]) 164 | reward = np.reshape(reward, [1, 1]) 165 | log_old_policy = np.reshape(log_old_policy, [1, 1]) 166 | 167 | state_batch.append(state) 168 | action_batch.append(action) 169 | reward_batch.append((reward+8)/8) 170 | old_policy_batch.append(log_old_policy) 171 | 172 | if len(state_batch) >= args.update_interval or done: 173 | states = self.list_to_batch(state_batch) 174 | actions = self.list_to_batch(action_batch) 175 | rewards = self.list_to_batch(reward_batch) 176 | old_policys = self.list_to_batch(old_policy_batch) 177 | 178 | v_values = self.critic.model.predict(states) 179 | next_v_value = self.critic.model.predict(next_state) 180 | 181 | gaes, td_targets = self.gae_target( 182 | rewards, v_values, next_v_value, done) 183 | 184 | for epoch in range(args.epochs): 185 | actor_loss = self.actor.train( 186 | old_policys, states, actions, gaes) 187 | critic_loss = self.critic.train(states, td_targets) 188 | 189 | state_batch = [] 190 | action_batch = [] 191 | reward_batch = [] 192 | old_policy_batch = [] 193 | 194 | episode_reward += reward[0][0] 195 | state = next_state[0] 196 | 197 | print('EP{} EpisodeReward={}'.format(ep, episode_reward)) 198 | wandb.log({'Reward': episode_reward}) 199 | 200 | 201 | def main(): 202 | env_name = 'Pendulum-v0' 203 | env = gym.make(env_name) 204 | agent = Agent(env) 205 | agent.train() 206 | 207 | 208 | if __name__ == "__main__": 209 | main() 210 | -------------------------------------------------------------------------------- /PPO/PPO_Discrete.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import tensorflow as tf 3 | from tensorflow.keras.layers import Input, Dense 4 | 5 | import gym 6 | import argparse 7 | import numpy as np 8 | 9 | tf.keras.backend.set_floatx('float64') 10 | 11 | wandb.init(name='PPO', project="deep-rl-tf2") 12 | 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument('--gamma', type=float, default=0.99) 15 | parser.add_argument('--update_interval', type=int, default=5) 16 | parser.add_argument('--actor_lr', type=float, default=0.0005) 17 | parser.add_argument('--critic_lr', type=float, default=0.001) 18 | parser.add_argument('--clip_ratio', type=float, default=0.1) 19 | parser.add_argument('--lmbda', type=float, default=0.95) 20 | parser.add_argument('--epochs', type=int, default=3) 21 | 22 | args = parser.parse_args() 23 | 24 | 25 | class Actor: 26 | def __init__(self, state_dim, action_dim): 27 | self.state_dim = state_dim 28 | self.action_dim = action_dim 29 | self.model = self.create_model() 30 | self.opt = tf.keras.optimizers.Adam(args.actor_lr) 31 | 32 | def create_model(self): 33 | return tf.keras.Sequential([ 34 | Input((self.state_dim,)), 35 | Dense(32, activation='relu'), 36 | Dense(16, activation='relu'), 37 | Dense(self.action_dim, activation='softmax') 38 | ]) 39 | 40 | def compute_loss(self, old_policy, new_policy, actions, gaes): 41 | gaes = tf.stop_gradient(gaes) 42 | old_log_p = tf.math.log( 43 | tf.reduce_sum(old_policy * actions)) 44 | old_log_p = tf.stop_gradient(old_log_p) 45 | log_p = tf.math.log(tf.reduce_sum( 46 | new_policy * actions)) 47 | ratio = tf.math.exp(log_p - old_log_p) 48 | clipped_ratio = tf.clip_by_value( 49 | ratio, 1 - args.clip_ratio, 1 + args.clip_ratio) 50 | surrogate = -tf.minimum(ratio * gaes, clipped_ratio * gaes) 51 | return tf.reduce_mean(surrogate) 52 | 53 | def train(self, old_policy, states, actions, gaes): 54 | actions = tf.one_hot(actions, self.action_dim) 55 | actions = tf.reshape(actions, [-1, self.action_dim]) 56 | actions = tf.cast(actions, tf.float64) 57 | 58 | with tf.GradientTape() as tape: 59 | logits = self.model(states, training=True) 60 | loss = self.compute_loss(old_policy, logits, actions, gaes) 61 | grads = tape.gradient(loss, self.model.trainable_variables) 62 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 63 | return loss 64 | 65 | 66 | class Critic: 67 | def __init__(self, state_dim): 68 | self.state_dim = state_dim 69 | self.model = self.create_model() 70 | self.opt = tf.keras.optimizers.Adam(args.critic_lr) 71 | 72 | def create_model(self): 73 | return tf.keras.Sequential([ 74 | Input((self.state_dim,)), 75 | Dense(32, activation='relu'), 76 | Dense(16, activation='relu'), 77 | Dense(16, activation='relu'), 78 | Dense(1, activation='linear') 79 | ]) 80 | 81 | def compute_loss(self, v_pred, td_targets): 82 | mse = tf.keras.losses.MeanSquaredError() 83 | return mse(td_targets, v_pred) 84 | 85 | def train(self, states, td_targets): 86 | with tf.GradientTape() as tape: 87 | v_pred = self.model(states, training=True) 88 | assert v_pred.shape == td_targets.shape 89 | loss = self.compute_loss(v_pred, tf.stop_gradient(td_targets)) 90 | grads = tape.gradient(loss, self.model.trainable_variables) 91 | self.opt.apply_gradients(zip(grads, self.model.trainable_variables)) 92 | return loss 93 | 94 | 95 | class Agent: 96 | def __init__(self, env): 97 | self.env = env 98 | self.state_dim = self.env.observation_space.shape[0] 99 | self.action_dim = self.env.action_space.n 100 | 101 | self.actor = Actor(self.state_dim, self.action_dim) 102 | self.critic = Critic(self.state_dim) 103 | 104 | def gae_target(self, rewards, v_values, next_v_value, done): 105 | n_step_targets = np.zeros_like(rewards) 106 | gae = np.zeros_like(rewards) 107 | gae_cumulative = 0 108 | forward_val = 0 109 | 110 | if not done: 111 | forward_val = next_v_value 112 | 113 | for k in reversed(range(0, len(rewards))): 114 | delta = rewards[k] + args.gamma * forward_val - v_values[k] 115 | gae_cumulative = args.gamma * args.lmbda * gae_cumulative + delta 116 | gae[k] = gae_cumulative 117 | forward_val = v_values[k] 118 | n_step_targets[k] = gae[k] + v_values[k] 119 | return gae, n_step_targets 120 | 121 | def list_to_batch(self, list): 122 | batch = list[0] 123 | for elem in list[1:]: 124 | batch = np.append(batch, elem, axis=0) 125 | return batch 126 | 127 | def train(self, max_episodes=1000): 128 | for ep in range(max_episodes): 129 | state_batch = [] 130 | action_batch = [] 131 | reward_batch = [] 132 | old_policy_batch = [] 133 | 134 | episode_reward, done = 0, False 135 | 136 | state = self.env.reset() 137 | 138 | while not done: 139 | # self.env.render() 140 | probs = self.actor.model.predict( 141 | np.reshape(state, [1, self.state_dim])) 142 | action = np.random.choice(self.action_dim, p=probs[0]) 143 | 144 | next_state, reward, done, _ = self.env.step(action) 145 | 146 | state = np.reshape(state, [1, self.state_dim]) 147 | action = np.reshape(action, [1, 1]) 148 | next_state = np.reshape(next_state, [1, self.state_dim]) 149 | reward = np.reshape(reward, [1, 1]) 150 | 151 | state_batch.append(state) 152 | action_batch.append(action) 153 | reward_batch.append(reward * 0.01) 154 | old_policy_batch.append(probs) 155 | 156 | if len(state_batch) >= args.update_interval or done: 157 | states = self.list_to_batch(state_batch) 158 | actions = self.list_to_batch(action_batch) 159 | rewards = self.list_to_batch(reward_batch) 160 | old_policys = self.list_to_batch(old_policy_batch) 161 | 162 | v_values = self.critic.model.predict(states) 163 | next_v_value = self.critic.model.predict(next_state) 164 | 165 | gaes, td_targets = self.gae_target( 166 | rewards, v_values, next_v_value, done) 167 | 168 | for epoch in range(args.epochs): 169 | actor_loss = self.actor.train( 170 | old_policys, states, actions, gaes) 171 | critic_loss = self.critic.train(states, td_targets) 172 | 173 | state_batch = [] 174 | action_batch = [] 175 | reward_batch = [] 176 | old_policy_batch = [] 177 | 178 | episode_reward += reward[0][0] 179 | state = next_state[0] 180 | 181 | print('EP{} EpisodeReward={}'.format(ep, episode_reward)) 182 | wandb.log({'Reward': episode_reward}) 183 | 184 | 185 | def main(): 186 | env_name = 'CartPole-v1' 187 | env = gym.make(env_name) 188 | agent = Agent(env) 189 | agent.train() 190 | 191 | 192 | if __name__ == "__main__": 193 | main() 194 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![TF Depend](https://img.shields.io/badge/TensorFlow-2.1-orange) ![GYM Depend](https://img.shields.io/badge/openai%2Fgym-0.17.1-blue) ![License Badge](https://img.shields.io/badge/license-Apache%202-green)
2 | 3 |

4 | 5 |

6 | 7 |

Deep Reinforcement Learning in TensorFlow2

8 | 9 | [DeepRL-TensorFlow2](https://github.com/marload/DeepRL-TensorFlow2) is a repository that implements a variety of popular Deep Reinforcement Learning algorithms using [TensorFlow2](https://tensorflow.org). The key to this repository is an easy-to-understand code. Therefore, if you are a student or a researcher studying Deep Reinforcement Learning, I think it would be the **best choice to study** with this repository. One algorithm relies only on one python script file. So you don't have to go in and out of different files to study specific algorithms. This repository is constantly being updated and will continue to add a new Deep Reinforcement Learning algorithm. 10 | 11 |

12 | 13 |

14 | 15 | ## Algorithms 16 | 17 | - [DQN](#dqn) 18 | - [DRQN](#drqn) 19 | - [DoubleDQN](#double_dqn) 20 | - [DuelingDQN](#dueling_dqn) 21 | - [A2C](#a2c) 22 | - [A3C](#a3c) 23 | - [PPO](#ppo) 24 | - [TRPO](#trpo) 25 | - [DDPG](#ddpg) 26 | - [TD3](#td3) 27 | - [SAC](#sac) 28 | 29 |
30 | 31 | 32 | 33 | ### DQN 34 | 35 | **Paper** [Playing Atari with Deep Reinforcement Learning](https://arxiv.org/abs/1312.5602)
36 | **Author** Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
37 | **Method** OFF-Policy / Temporal-Diffrence / Model-Free
38 | **Action** Discrete only
39 | 40 | #### Core of Idea 41 | ```python 42 | # idea01. Approximate Q-Function using NeuralNetwork 43 | def create_model(self): 44 | model = tf.keras.Sequential([ 45 | Input((self.state_dim,)), 46 | Dense(32, activation='relu'), 47 | Dense(16, activation='relu'), 48 | Dense(self.action_dim) 49 | ]) 50 | model.compile(loss='mse', optimizer=Adam(args.lr)) 51 | return model 52 | 53 | # idea02. Use target network 54 | self.target_model = ActionStateModel(self.state_dim, self.action_dim) 55 | 56 | # idea03. Use ReplayBuffer to increase data efficiency 57 | class ReplayBuffer: 58 | def __init__(self, capacity=10000): 59 | self.buffer = deque(maxlen=capacity) 60 | 61 | def put(self, state, action, reward, next_state, done): 62 | self.buffer.append([state, action, reward, next_state, done]) 63 | 64 | def sample(self): 65 | sample = random.sample(self.buffer, args.batch_size) 66 | states, actions, rewards, next_states, done = map(np.asarray, zip(*sample)) 67 | states = np.array(states).reshape(args.batch_size, -1) 68 | next_states = np.array(next_states).reshape(args.batch_size, -1) 69 | return states, actions, rewards, next_states, done 70 | 71 | def size(self): 72 | return len(self.buffer) 73 | ``` 74 | 75 | #### Getting Start 76 | ```bash 77 | # Discrete Action Space Deep Q-Learning 78 | $ python DQN/DQN_Discrete.py 79 | ``` 80 | 81 |
82 | 83 | 84 | 85 | 86 | ### DRQN 87 | 88 | **Paper** [Deep Recurrent Q-Learning for Partially Observable MDPs](https://arxiv.org/abs/1507.06527)
89 | **Author** Matthew Hausknecht, Peter Stone
90 | **Method** OFF-Policy / Temporal-Diffrence / Model-Free
91 | **Action** Discrete only
92 | 93 | #### Core of Ideas 94 | ```python 95 | # idea01. Previous state uses LSTM layer as feature 96 | def create_model(self): 97 | return tf.keras.Sequential([ 98 | Input((args.time_steps, self.state_dim)), 99 | LSTM(32, activation='tanh'), 100 | Dense(16, activation='relu'), 101 | Dense(self.action_dim) 102 | ]) 103 | ``` 104 | 105 | #### Getting Start 106 | ```bash 107 | # Discrete Action Space Deep Recurrent Q-Learning 108 | $ python DRQN/DRQN_Discrete.py 109 | ``` 110 | 111 |
112 | 113 | 114 | 115 | 116 | ### DoubleDQN 117 | 118 | **Paper** [Deep Reinforcement Learning with Double Q-learning](https://arxiv.org/abs/1509.06461)
119 | **Author** Hado van Hasselt, Arthur Guez, David Silver
120 | **Method** OFF-Policy / Temporal-Diffrence / Model-Free
121 | **Action** Discrete only
122 | 123 | #### Core of Ideas 124 | ```python 125 | # idea01. Resolved the issue of 'overestimate' in Q Learning 126 | on_action = np.argmax(self.model.predict(next_states), axis=1) 127 | next_q_values = self.target_model.predict(next_states)[range(args.batch_size), on_action] 128 | targets[range(args.batch_size), actions] = rewards + (1-done) * next_q_values * args.gamma 129 | ``` 130 | 131 | #### Getting Start 132 | ```bash 133 | # Discrete Action Space Double Deep Q-Learning 134 | $ python DoubleQN/DoubleDQN_Discrete.py 135 | ``` 136 | 137 |
138 | 139 | 140 | 141 | ### DuelingDQN 142 | 143 | **Paper** [Dueling Network Architectures for Deep Reinforcement Learning](https://arxiv.org/abs/1511.06581)
144 | **Author** Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
145 | **Method** OFF-Policy / Temporal-Diffrence / Model-Free
146 | **Action** Discrete only
147 | 148 | #### Core of Ideas 149 | ```python 150 | # idea01. Q-Function has been separated into Value Function and Advantage Function 151 | def create_model(self): 152 | backbone = tf.keras.Sequential([ 153 | Input((self.state_dim,)), 154 | Dense(32, activation='relu'), 155 | Dense(16, activation='relu') 156 | ]) 157 | state_input = Input((self.state_dim,)) 158 | backbone_1 = Dense(32, activation='relu')(state_input) 159 | backbone_2 = Dense(16, activation='relu')(backbone_1) 160 | value_output = Dense(1)(backbone_2) 161 | advantage_output = Dense(self.action_dim)(backbone_2) 162 | output = Add()([value_output, advantage_output]) 163 | model = tf.keras.Model(state_input, output) 164 | model.compile(loss='mse', optimizer=Adam(args.lr)) 165 | return model 166 | ``` 167 | 168 | #### Gettting Start 169 | ```bash 170 | # Discrete Action Space Dueling Deep Q-Learning 171 | $ python DuelingDQN/DuelingDQN_Discrete.py 172 | ``` 173 | 174 |
175 | 176 | 177 | 178 | ### A2C 179 | 180 | **Paper** [Actor-Critic Algorithms](https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf)
181 | **Author** Vijay R. Konda, John N. Tsitsiklis
182 | **Method** ON-Policy / Temporal-Diffrence / Model-Free
183 | **Action** Discrete, Continuous
184 | 185 | #### Core of Ideas 186 | ```python 187 | # idea01. Use Advantage to reduce Variance 188 | def advatnage(self, td_targets, baselines): 189 | return td_targets - baselines 190 | ``` 191 | 192 | #### Getting Start 193 | ```bash 194 | # Discrete Action Space Advantage Actor-Critic 195 | $ python A2C/A2C_Discrete.py 196 | 197 | # Continuous Action Space Advantage Actor-Critic 198 | $ python A2C/A2C_Continuous.py 199 | ``` 200 | 201 |
202 | 203 | 204 | 205 | ### A3C 206 | 207 | **Paper** [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783)
208 | **Author** Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
209 | **Method** ON-Policy / Temporal-Diffrence / Model-Free
210 | **Action** Discrete, Continuous
211 | 212 | #### Core of Ideas 213 | ```python 214 | # idea01. Reduce the correlation of data by running asynchronously multiple workers 215 | def train(self, max_episodes=1000): 216 | workers = [] 217 | 218 | for i in range(self.num_workers): 219 | env = gym.make(self.env_name) 220 | workers.append(WorkerAgent( 221 | env, self.global_actor, self.global_critic, max_episodes)) 222 | 223 | for worker in workers: 224 | worker.start() 225 | 226 | for worker in workers: 227 | worker.join() 228 | 229 | # idea02. Improves exploration through entropy loss 230 | entropy_loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True) 231 | ``` 232 | 233 | #### Getting Start 234 | ```bash 235 | # Discrete Action Space Asyncronous Advantage Actor-Critic 236 | $ python A3C/A3C_Discrete.py 237 | 238 | # Continuous Action Space Asyncronous Advantage Actor-Critic 239 | $ python A3C/A3C_Continuous.py 240 | ``` 241 | 242 |
243 | 244 | 245 | 246 | ### PPO 247 | 248 | **Paper** [Proximal Policy Optimization](https://arxiv.org/abs/1707.06347)
249 | **Author** John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
250 | **Method** ON-Policy / Temporal-Diffrence / Model-Free
251 | **Action** Discrete, Continuous
252 | 253 | #### Core of ideas 254 | ```python 255 | # idea01. Use Importance Sampling to act like an Off-Policy algorithm 256 | # idea02. Use clip to prevent rapid changes in parameters. 257 | def compute_loss(self, old_policy, new_policy, actions, gaes): 258 | gaes = tf.stop_gradient(gaes) 259 | old_log_p = tf.math.log( 260 | tf.reduce_sum(old_policy * actions)) 261 | old_log_p = tf.stop_gradient(old_log_p) 262 | log_p = tf.math.log(tf.reduce_sum( 263 | new_policy * actions)) 264 | ratio = tf.math.exp(log_p - old_log_p) 265 | clipped_ratio = tf.clip_by_value( 266 | ratio, 1 - args.clip_ratio, 1 + args.clip_ratio) 267 | surrogate = -tf.minimum(ratio * gaes, clipped_ratio * gaes) 268 | return tf.reduce_mean(surrogate) 269 | ``` 270 | 271 | #### Getting Start 272 | ```bash 273 | # Discrete Action Space Proximal Policy Optimization 274 | $ python PPO/PPO_Discrete.py 275 | 276 | # Continuous Action Space Proximal Policy Optimization 277 | $ python PPO/PPO_Continuous.py 278 | ``` 279 | 280 |
281 | 282 | 283 | 284 | ### DDPG 285 | 286 | **Paper** [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)
287 | **Author** Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
288 | **Method** OFF-Policy / Temporal-Diffrence / Model-Free
289 | **Action** Continuous
290 | 291 | #### Core of ideas 292 | ```python 293 | # idea01. Use deterministic Actor Model 294 | def create_model(self): 295 | return tf.keras.Sequential([ 296 | Input((self.state_dim,)), 297 | Dense(32, activation='relu'), 298 | Dense(32, activation='relu'), 299 | Dense(self.action_dim, activation='tanh'), 300 | Lambda(lambda x: x * self.action_bound) 301 | ]) 302 | 303 | # idea02. Add noise to Action 304 | action = np.clip(action + noise, -self.action_bound, self.action_bound) 305 | ``` 306 | 307 | #### Getting Start 308 | ```bash 309 | # Continuous Action Space Proximal Policy Optimization 310 | $ python DDPG/DDPG_Continuous.py 311 | ``` 312 | 313 |
314 | 315 | 316 | 317 | ### TRPO 318 | 319 | **Paper** [Trust Region Policy Optimization](https://arxiv.org/abs/1502.05477)
320 | **Author** John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel
321 | **Method** OFF-Policy / Temporal-Diffrence / Model-Free
322 | **Action** Discrete, Continuous
323 | 324 | ```bash 325 | # NOTE: Not yet implemented! 326 | ``` 327 | 328 |
329 | 330 | 331 | 332 | ### TD3 333 | 334 | **Paper** [Addressing Function Approximation Error in Actor-Critic Methods](https://arxiv.org/abs/1802.09477)
335 | **Author** Scott Fujimoto, Herke van Hoof, David Meger
336 | **Method** OFF-Policy / Temporal-Diffrence / Model-Free
337 | **Action** Continuous
338 | 339 | ```bash 340 | # NOTE: Not yet implemented! 341 | ``` 342 | 343 |
344 | 345 | 346 | 347 | ### SAC 348 | 349 | **Paper** [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 350 | ](https://arxiv.org/abs/1801.01290)
351 | **Author** Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine
352 | **Method** OFF-Policy / Temporal-Diffrence / Model-Free
353 | **Action** Discrete, Continuous
354 | 355 | ```bash 356 | # NOTE: Not yet implemented! 357 | ``` 358 | 359 |
360 | 361 | ## Reference 362 | 363 | - https://github.com/carpedm20/deep-rl-tensorflow 364 | - https://github.com/Yeachan-Heo/Reinforcement-Learning-Book 365 | - https://github.com/pasus/Reinforcement-Learning-Book 366 | - https://github.com/vcadillog/PPO-Mario-Bros-Tensorflow-2 367 | - https://spinningup.openai.com/en/latest/spinningup/keypapers.html 368 | - https://github.com/seungeunrho/minimalRL 369 | - https://github.com/openai/baselines 370 | - https://github.com/anita-hu/TF2-RL 371 | -------------------------------------------------------------------------------- /assets/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/archsyscall/DeepRL-TensorFlow2/876266d9a5fcf7d8a7c7e3afd8b110085b32b615/assets/.DS_Store -------------------------------------------------------------------------------- /assets/discrete_reward_plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/archsyscall/DeepRL-TensorFlow2/876266d9a5fcf7d8a7c7e3afd8b110085b32b615/assets/discrete_reward_plot.png -------------------------------------------------------------------------------- /assets/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/archsyscall/DeepRL-TensorFlow2/876266d9a5fcf7d8a7c7e3afd8b110085b32b615/assets/logo.png --------------------------------------------------------------------------------