├── .gitignore ├── __init__.py ├── gae.py ├── images ├── baselines_lunar.png ├── baselines_pong.png ├── my_lunar.png └── my_pong.png ├── main.py ├── model.py ├── pyproject.toml ├── readme.md ├── running_mean_std.py ├── tracker.py ├── train.py ├── util.py ├── uv.lock └── worker.py /.gitignore: -------------------------------------------------------------------------------- 1 | .word_vectors_cache/ 2 | .streamlit/ 3 | data/ 4 | *.swp 5 | *.prof 6 | *.swo 7 | *.log 8 | *.hdf5 9 | *.pickle 10 | *.swn 11 | _cache__home_zplizzi_data_lsun_bedroom_train_lmdb 12 | _cache__datavol_data_lsun_bedroom_train_lmdb 13 | _cache_datavoldatalsunbedroomtrainlmdb 14 | wandb/ 15 | *.DS_Store 16 | 17 | 18 | 19 | # Created by https://www.gitignore.io/api/python,jupyternotebook 20 | 21 | ### JupyterNotebook ### 22 | .ipynb_checkpoints 23 | */.ipynb_checkpoints/* 24 | 25 | # Remove previous ipynb_checkpoints 26 | # git rm -r .ipynb_checkpoints/ 27 | # 28 | 29 | ### Python ### 30 | # Byte-compiled / optimized / DLL files 31 | __pycache__/ 32 | *.py[cod] 33 | *$py.class 34 | 35 | # C extensions 36 | *.so 37 | 38 | # Distribution / packaging 39 | .Python 40 | build/ 41 | develop-eggs/ 42 | dist/ 43 | downloads/ 44 | eggs/ 45 | .eggs/ 46 | lib64/ 47 | parts/ 48 | sdist/ 49 | var/ 50 | wheels/ 51 | *.egg-info/ 52 | .installed.cfg 53 | *.egg 54 | MANIFEST 55 | 56 | # PyInstaller 57 | # Usually these files are written by a python script from a template 58 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 59 | *.manifest 60 | *.spec 61 | 62 | # Installer logs 63 | pip-log.txt 64 | pip-delete-this-directory.txt 65 | 66 | # Unit test / coverage reports 67 | htmlcov/ 68 | .tox/ 69 | .coverage 70 | .coverage.* 71 | .cache 72 | nosetests.xml 73 | coverage.xml 74 | *.cover 75 | .hypothesis/ 76 | .pytest_cache/ 77 | 78 | # Translations 79 | *.mo 80 | *.pot 81 | 82 | # Django stuff: 83 | *.log 84 | local_settings.py 85 | db.sqlite3 86 | 87 | # Flask stuff: 88 | instance/ 89 | .webassets-cache 90 | 91 | # Scrapy stuff: 92 | .scrapy 93 | 94 | # Sphinx documentation 95 | docs/_build/ 96 | 97 | # PyBuilder 98 | target/ 99 | 100 | # Jupyter Notebook 101 | 102 | # pyenv 103 | .python-version 104 | 105 | # celery beat schedule file 106 | celerybeat-schedule 107 | 108 | # SageMath parsed files 109 | *.sage.py 110 | 111 | # Environments 112 | .env 113 | .venv 114 | env/ 115 | venv/ 116 | ENV/ 117 | env.bak/ 118 | venv.bak/ 119 | 120 | # Spyder project settings 121 | .spyderproject 122 | .spyproject 123 | 124 | # Rope project settings 125 | .ropeproject 126 | 127 | # mkdocs documentation 128 | /site 129 | 130 | # mypy 131 | .mypy_cache/ 132 | 133 | ### Python Patch ### 134 | .venv/ 135 | 136 | 137 | # End of https://www.gitignore.io/api/python,jupyternotebook 138 | 139 | .idea/ 140 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zplizzi/pytorch-ppo/2f6758ae2ef862ef38da806b092e22fb20edcf97/__init__.py -------------------------------------------------------------------------------- /gae.py: -------------------------------------------------------------------------------- 1 | """ 2 | Code for computing Generalized Advantage Estimation. 3 | """ 4 | import numpy as np 5 | 6 | 7 | def gae(rewards, values, episode_ends, gamma, lam): 8 | """Compute generalized advantage estimate. 9 | rewards: a list of rewards at each step. 10 | values: the value estimate of the state at each step. 11 | episode_ends: an array of the same shape as rewards, with a 1 if the 12 | episode ended at that step and a 0 otherwise. 13 | gamma: the discount factor. 14 | lam: the GAE lambda parameter. 15 | """ 16 | # Invert episode_ends to have 0 if the episode ended and 1 otherwise 17 | episode_ends = (episode_ends * -1) + 1 18 | 19 | N = rewards.shape[0] 20 | T = rewards.shape[1] 21 | gae_step = np.zeros((N, )) 22 | advantages = np.zeros((N, T)) 23 | for t in reversed(range(T - 1)): 24 | # First compute delta, which is the one-step TD error 25 | delta = rewards[:, t] + gamma * values[:, t + 1] * episode_ends[:, t] - values[:, t] 26 | # Then compute the current step's GAE by discounting the previous step 27 | # of GAE, resetting it to zero if the episode ended, and adding this 28 | # step's delta 29 | gae_step = delta + gamma * lam * episode_ends[:, t] * gae_step 30 | # And store it 31 | advantages[:, t] = gae_step 32 | return advantages 33 | -------------------------------------------------------------------------------- /images/baselines_lunar.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zplizzi/pytorch-ppo/2f6758ae2ef862ef38da806b092e22fb20edcf97/images/baselines_lunar.png -------------------------------------------------------------------------------- /images/baselines_pong.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zplizzi/pytorch-ppo/2f6758ae2ef862ef38da806b092e22fb20edcf97/images/baselines_pong.png -------------------------------------------------------------------------------- /images/my_lunar.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zplizzi/pytorch-ppo/2f6758ae2ef862ef38da806b092e22fb20edcf97/images/my_lunar.png -------------------------------------------------------------------------------- /images/my_pong.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zplizzi/pytorch-ppo/2f6758ae2ef862ef38da806b092e22fb20edcf97/images/my_pong.png -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | """The main entrypoint.""" 2 | import argparse 3 | import ctypes 4 | import multiprocessing as mp 5 | 6 | import numpy as np 7 | import torch 8 | 9 | from model import MLPBase, CNNBase, Discrete, Normal 10 | from train import train_step 11 | from worker import GamePlayer 12 | from gae import gae 13 | from util import get_gym_env_info 14 | from running_mean_std import RunningMeanStd, apply_normalizer 15 | from tracker import WandBTracker, ConsoleTracker, NoTracker 16 | import gymnasium as gym 17 | import ale_py 18 | 19 | if __name__ == "__main__": 20 | parser = argparse.ArgumentParser() 21 | parser.add_argument('--name') 22 | parser.add_argument('--env_name', default="PongNoFrameskip-v4") 23 | parser.add_argument('--model', default="mlp") 24 | parser.add_argument('--gamma', default=.99, type=float) 25 | parser.add_argument('--lam', default=.95, type=float) 26 | parser.add_argument('--epsilon', default=.1, type=float) 27 | parser.add_argument('--value_loss_coef', default=.5, type=float) 28 | parser.add_argument('--entropy_coef', default=.01, type=float) 29 | parser.add_argument('--num_workers', default=8, type=int) 30 | parser.add_argument('--num_iterations', default=10**10, type=int) 31 | parser.add_argument('--num_steps', default=128, type=int) 32 | parser.add_argument('--ppo_epochs', default=4, type=int) 33 | parser.add_argument('--num_batches', default=4, type=int) 34 | parser.add_argument('--lr', default=2.5e-4, type=float) 35 | parser.add_argument('--device', default="cpu") 36 | parser.add_argument('--end_on_life_loss', default=False) 37 | parser.add_argument('--clip_rewards', default=False) 38 | parser.add_argument('--logger', default="console") 39 | args = parser.parse_args() 40 | 41 | gym.register_envs(ale_py) 42 | # this is required so that we share the shared array properly. mac eg default is spawn. 43 | mp.set_start_method('fork') 44 | 45 | args.batch_size = int(args.num_workers / args.num_batches) 46 | assert args.batch_size > 0 47 | args.num_actions, args.obs_shape, args.num_obs, action_type = \ 48 | get_gym_env_info(args.env_name) 49 | device = torch.device(args.device) 50 | 51 | # Define common shapes for convenience 52 | scalar_shape = (args.num_workers, args.num_steps) 53 | if args.model == "cnn": 54 | # This is the shape after preprocessing (reshape + greyscale) 55 | batch_obs_shape = (args.num_workers, args.num_steps, 1, 84, 84) 56 | args.steps_to_skip = 4 57 | args.end_on_life_loss = True 58 | elif args.model == "mlp": 59 | batch_obs_shape = (args.num_workers, args.num_steps, args.num_obs) 60 | args.steps_to_skip = 1 61 | else: 62 | raise ValueError("Model not recognized") 63 | 64 | 65 | # Make a shared array to get observations from each process 66 | # and wrap it with Numpy 67 | shared_obs_c = mp.Array(ctypes.c_float, int(np.prod(batch_obs_shape))) 68 | shared_obs = np.frombuffer(shared_obs_c.get_obj(), dtype=np.float32) 69 | shared_obs = np.reshape(shared_obs, batch_obs_shape) 70 | 71 | # Make arrays to store all other rollout info 72 | rewards = np.zeros(scalar_shape, dtype=np.float32) 73 | discounted_rewards = np.zeros(scalar_shape, dtype=np.float32) 74 | episode_ends = np.zeros(scalar_shape, dtype=np.float32) 75 | values = np.zeros(scalar_shape, dtype=np.float32) 76 | policy_probs = np.zeros(scalar_shape, dtype=np.float32) 77 | if action_type == "continuous": 78 | action_shape = (args.num_workers, args.num_steps, args.num_actions) 79 | actions = np.zeros(action_shape, dtype=np.float32) 80 | elif action_type == "discrete": 81 | actions = np.zeros(scalar_shape, dtype=np.int32) 82 | else: 83 | raise ValueError("Action type not recognized") 84 | 85 | # Build the key classes 86 | if args.logger == "wandb": 87 | tracker = WandBTracker(args.name, args) 88 | elif args.logger == "console": 89 | tracker = ConsoleTracker(args.name, args) 90 | elif args.logger == "none": 91 | tracker = NoTracker(args.name, args) 92 | else: 93 | raise ValueError("Logger not recognized") 94 | game_player = GamePlayer(args, shared_obs) 95 | if action_type == "discrete": 96 | dist = Discrete(args.num_actions) 97 | elif action_type == "continuous": 98 | dist = Normal(args.num_actions) 99 | else: 100 | raise ValueError("Action type not recognized") 101 | 102 | if args.model == "cnn": 103 | model = CNNBase(1, args.num_actions, dist).to(device) 104 | elif args.model == "mlp": 105 | model = MLPBase(args.num_obs, args.num_actions, dist).to(device) 106 | else: 107 | raise ValueError("Model not recognized") 108 | 109 | optim = torch.optim.Adam(model.parameters(), lr=args.lr) 110 | reward_normalizer = RunningMeanStd(shape=()) 111 | obs_normalizer = RunningMeanStd(shape=(args.num_obs, )) 112 | 113 | # Main loop 114 | i = 0 115 | for i in range(args.num_iterations): 116 | # Run num_steps of the game in each worker and accumulate results in 117 | # the data arrays 118 | game_player.run_rollout(args, shared_obs, rewards, discounted_rewards, 119 | values, policy_probs, actions, model, 120 | obs_normalizer, device, episode_ends) 121 | 122 | observations = shared_obs.copy() 123 | 124 | if args.model == "mlp": 125 | # Normalize rewards 126 | rewards = apply_normalizer(rewards, reward_normalizer, 127 | update_data=discounted_rewards, 128 | center=False) 129 | 130 | # Compute advantages and future discounted rewards with GAE 131 | advantages = gae(rewards, values, episode_ends, args.gamma, args.lam) 132 | advantages = advantages.astype(np.float32) 133 | rewards_to_go = advantages + values 134 | 135 | # normalize advantages 136 | raw_advantages = advantages.copy() 137 | advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-5) 138 | 139 | train_data = [advantages, rewards_to_go, values, actions, observations, 140 | policy_probs] 141 | train_data = [torch.tensor(x).to(device) for x in train_data] 142 | 143 | # Split the data into batches in the num_workers dimension 144 | for epoch in range(args.ppo_epochs): 145 | for batch in range(args.num_batches): 146 | start = batch * args.batch_size 147 | end = (batch + 1) * args.batch_size 148 | 149 | # slice out batches from train_data 150 | batch_data = [x[start:end] for x in train_data] 151 | 152 | # flatten (batch_size,num_steps,...) into ((batch_size*num_steps,...) 153 | batch_data = [x.reshape((-1, ) + x.shape[2:]) for x in batch_data] 154 | 155 | # Step batch 156 | train_step(model, optim, batch_data, args, i, tracker) 157 | 158 | tracker.log_iteration_time(args.num_workers * args.num_steps, i) 159 | if i % 5 == 0: 160 | tracker.add_histogram("episode/episode_length", 161 | game_player.episode_length, i) 162 | tracker.add_histogram("episode/episode_rewards", 163 | game_player.episode_rewards, i) 164 | if i % 25 == 0: 165 | tracker.add_histogram("training/raw_advantages", 166 | raw_advantages, i) 167 | tracker.add_histogram("training/rewards", 168 | rewards, i) 169 | tracker.add_histogram("training/observations", 170 | observations, i) 171 | tracker.add_histogram("training/reward_std", 172 | np.sqrt(reward_normalizer.var), i) 173 | if args.model == "cnn": 174 | tracker.add_image("images/obs", observations[0, 0, 0], i) 175 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | """ 2 | Model definitions. 3 | 4 | These are modified versions of the models from 5 | https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/model.py 6 | """ 7 | import numpy as np 8 | import torch 9 | import torch.nn as nn 10 | 11 | 12 | def init(module, weight_init, bias_init, gain=1): 13 | """Helper to initialize a layer weight and bias.""" 14 | weight_init(module.weight.data, gain=gain) 15 | bias_init(module.bias.data) 16 | return module 17 | 18 | 19 | class Flatten(nn.Module): 20 | """Helper to flatten a tensor.""" 21 | def forward(self, x): 22 | return x.view(x.size(0), -1) 23 | 24 | 25 | class CNNBase(nn.Module): 26 | """CNN model.""" 27 | def __init__(self, num_channels, num_outputs, dist, hidden_size=512): 28 | """Initializer. 29 | num_channels: the number of channels in the input images (eg 3 30 | for RGB images, or 12 for a stack of 4 RGB images). 31 | num_outputs: the dimension of the output distribution. 32 | dist: the output distribution (eg Discrete or Normal). 33 | hidden_size: the size of the final actor+critic linear layers 34 | """ 35 | super().__init__() 36 | 37 | init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init. 38 | constant_(x, 0), nn.init.calculate_gain('relu')) 39 | 40 | self.main = nn.Sequential( 41 | init_(nn.Conv2d(num_channels, 32, kernel_size=8, stride=4)), 42 | nn.ReLU(), 43 | init_(nn.Conv2d(32, 64, kernel_size=4, stride=2)), 44 | nn.ReLU(), 45 | init_(nn.Conv2d(64, 32, kernel_size=3, stride=1)), 46 | nn.ReLU(), 47 | Flatten(), 48 | init_(nn.Linear(32 * 7 * 7, hidden_size)), 49 | nn.ReLU() 50 | ) 51 | 52 | init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init. 53 | constant_(x, 0)) 54 | 55 | self.critic_linear = init_(nn.Linear(hidden_size, 1)) 56 | self.actor_linear = init_(nn.Linear(hidden_size, num_outputs)) 57 | self.dist = dist 58 | 59 | def forward(self, x): 60 | """x should have shape (batch_size, num_channels, 84, 84).""" 61 | x = self.main(x) 62 | value = self.critic_linear(x) 63 | action_logits = self.actor_linear(x) 64 | return value, self.dist(action_logits) 65 | 66 | 67 | class MLPBase(nn.Module): 68 | """Basic multi-layer linear model.""" 69 | def __init__(self, num_inputs, num_outputs, dist, hidden_size=64): 70 | super().__init__() 71 | 72 | init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init. 73 | constant_(x, 0), np.sqrt(2)) 74 | 75 | init2_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init. 76 | constant_(x, 0)) 77 | 78 | self.actor = nn.Sequential( 79 | init_(nn.Linear(num_inputs, hidden_size)), nn.Tanh(), 80 | init_(nn.Linear(hidden_size, hidden_size)), nn.Tanh(), 81 | init2_(nn.Linear(hidden_size, num_outputs))) 82 | 83 | self.critic = nn.Sequential( 84 | init_(nn.Linear(num_inputs, hidden_size)), nn.Tanh(), 85 | init_(nn.Linear(hidden_size, hidden_size)), nn.Tanh(), 86 | init_(nn.Linear(hidden_size, 1))) 87 | 88 | self.dist = dist 89 | 90 | def forward(self, x): 91 | value = self.critic(x) 92 | action_logits = self.actor(x) 93 | return value, self.dist(action_logits) 94 | 95 | 96 | class Discrete(nn.Module): 97 | """A module that builds a Categorical distribution from logits.""" 98 | def __init__(self, num_outputs): 99 | super().__init__() 100 | 101 | def forward(self, x): 102 | # Do softmax on the proper dimension with either batched or non 103 | # batched inputs 104 | if len(x.shape) == 3: 105 | probs = nn.functional.softmax(x, dim=2) 106 | elif len(x.shape) == 2: 107 | probs = nn.functional.softmax(x, dim=1) 108 | else: 109 | print(x.shape) 110 | raise 111 | dist = torch.distributions.Categorical(probs=probs) 112 | return dist 113 | 114 | 115 | class Normal(nn.Module): 116 | """A module that builds a Diagonal Gaussian distribution from means. 117 | 118 | Standard deviations are learned parameters in this module. 119 | """ 120 | def __init__(self, num_outputs): 121 | super().__init__() 122 | # initial variance is e^0 = 1 123 | self.stds = nn.Parameter(torch.zeros(num_outputs)) 124 | 125 | def forward(self, x): 126 | dist = torch.distributions.Normal(loc=x, scale=self.stds.exp()) 127 | 128 | # By default we get the probability of sampling each dimension of the 129 | # distribution. The full probability is the product of these, or 130 | # the sum since we're working with log probabilities. 131 | # So overwrite the log_prob function to handle this for us 132 | dist.old_log_prob = dist.log_prob 133 | dist.log_prob = lambda x: dist.old_log_prob(x).sum(-1) 134 | 135 | return dist 136 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "pytorch-ppo" 3 | version = "0.1.0" 4 | description = "Simple PPO implementation" 5 | readme = "README.md" 6 | requires-python = ">=3.11" 7 | dependencies = [ 8 | "gymnasium[atari,box2d]>=1.1.1", 9 | "numpy>=2.2.5", 10 | "torch>=2.7.0", 11 | "torchvision>=0.22.0", 12 | "wandb>=0.19.10", 13 | ] 14 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # Pytorch PPO 2 | Simple, readable, yet full-featured implementation of PPO in Pytorch 3 | 4 | ## Motivation 5 | 6 | The aim of this repository is to provide a minimal yet performant implementation of PPO in Pytorch. Mostly I wrote it just for practice, but also because all the major implementations of PPO are buried in large, complex, and minimally-commented repositories. 7 | 8 | This implementation is only ~500 lines, yet includes all the various implementation tricks and details to get comparable performance (accuracy and speed) to the major implementations (eg OpenAI's baselines repo). All the key algorithmic code is in this repo - there aren't dependencies to other repos for normalization, advantage estimation, etc. 9 | 10 | It supports: 11 | - Continuous and discrete action spaces 12 | - Low-dimensional state spaces with a MLP and high-dimensional image-based state spaces with a CNN 13 | 14 | This means it should be able to run the majority of environments in the OpenAI Gym - I've tested it with the Box2d environments and the Atari environments. 15 | 16 | It is a parallel implementation, capable of spawning multiple processes to run the environment simulations and with each process recording observations into shared memory for maximum performance. 17 | 18 | ## Installation and Usage 19 | 20 | Using uv is suggested. Simply prepend all commands with `uv run` and the dependencies should be setup properly. 21 | On mac, you will need to `brew install swig` first, on Linux, you will need to `apt-get install swig` first. 22 | 23 | The key dependencies are the OpenAI Gym and Pytorch. It runs on CPU or CPU+GPU. For MuJoCo environments, you'll need that (proprietary) dependency also. 24 | 25 | You can run the code with by running `main.py` with any desired arguments, eg 26 | `main.py --env_name="LunarLander-v3" --model="mlp"`. 27 | 28 | You must make sure that the model type (`mlp` or `cnn`) matches the environment you're training on. 29 | 30 | It will default to running on CPU. To use GPU, use the flag `--device="cuda"`. To run on mac, use `--device="mps"`. 31 | 32 | I'd highly recommend using Weights and Biases for detailed logging. Use the flag `logger="wandb"`. 33 | 34 | All available arguments can be viewed by running `main.py --help`. 35 | 36 | I suggest trying out the Box2d environments such as `LunarLander-v3` and `BipedalWalker-v2` as they're free (unlike MuJoCo) and are what I've tested with. The Atari environments are also included with Gym, but take longer to train. `PongNoFrameskip-v4` is a simple one to test. 37 | 38 | ## Performace comparison to OpenAI Baselines 39 | 40 | To be sure that the performance is comparable to existing implementations, I instrumented the baselines repo with more detailed logging code. I ran both with as similar hyperparameters as I could. 41 | 42 | The charts are histograms of complete episode returns per step of the algorithm (one step is a batch of 8 partial episodes, each partial episode with 128 timesteps). 43 | 44 | Check out the full, detailed metrics at the "all logging data" links below the charts. 45 | 46 | ### `LunarLander-v3` 47 | 48 | #### OpenAI Baselines 49 | `python -m baselines.run --alg=ppo2 --network=mlp --num_timesteps=2e8 --ent_coef=0.01 --num_hidden=64 --num_layers=3 --value_network=copy --num_env=12 --lr=2.5e-4 --vf_coef=.5 --max_grad_norm=.5 --gamma=.99 --lam=.95 --nminibatches=4 --noptepochs=4 --cliprange=.1 --nsteps=128 --env="LunarLander-v3"` 50 | ![](images/baselines_lunar.png) 51 | 52 | #### This code 53 | `python main.py --env="LunarLander-v3" --model="mlp"` 54 | ![](images/my_lunar.png) 55 | 56 | ### `PongNoFrameskip-v4` 57 | 58 | I didn't spend as much effort making sure my CNN model and hyperparameters are exactly the same as Baselines, so these charts don't match as well as the with LunarLander. However, the overall final performance and time to convergence are similar. 59 | 60 | #### OpenAI Baselines 61 | `export CUDA_VISIBLE_DEVICES=1 && python -m baselines.run --alg=ppo2 --network=cnn --num_timesteps=2e8 --ent_coef=0.01 --num_env=12 --lr=2.5e-4 --vf_coef=.5 --max_grad_norm=.5 --gamma=.99 --lam=.95 --nminibatches=4 --noptepochs=4 --cliprange=.1 --nsteps=128 --env="PongNoFrameskip-v4"` 62 | ![](images/baselines_pong.png) 63 | 64 | #### This code 65 | `python main.py --env="PongNoFrameskip-v4" --model="cnn" --end_on_life_loss=True --clip_rewards=True` 66 | ![](images/my_pong.png) 67 | 68 | 69 | ### Speed comparison 70 | 71 | When training on `PongNoFrameskip-v4`, this code processes 1400 frames per second (on a GCP V100 server). The OpenAI baselines repository, running PPO on the same environment, a similar model, and the same hardware, reported 1600 frames per second. 72 | 73 | ## Implementation Details 74 | 75 | Beyond the standard PPO algorithm, there are some implementation details drawn from the baselines repo that turned out to be important in matching the performance: 76 | - Using Generalized Advantage Estimation for advantages and cumulative discounted future rewards 77 | - Using multiple parallel environment rollouts 78 | - Using value clipping with a PPO-style confidence interval region 79 | - Normalizing the advantages, observations, and rewards (each with different methods) for MLP 80 | - Advantages are normalized per-iteration by mean centering and dividing by the standard deviation 81 | - Observations are normalized by mean centering and dividing by the standard deviation using cumulative statistics computed from the start of training 82 | - Per-step rewards are normalized by dividing by the standard deviation of the discounted cumulative rewards using cumulative statistics computed from the start of training 83 | - Using an orthogonal initialization of the layer weights 84 | - Using tanh nonlinearities in the MLP 85 | - For Atari, there are a variety of other tricks: 86 | - Clipping the per-step rewards to {-1, 0, 1} 87 | - Skipping 3 environment steps for every step used for training 88 | - Converting the images to 1-channel grayscale, warping to 84x84, and rescaling to [0, 1], but no further normalization 89 | - Don't normalize rewards at all, beyond the clipping 90 | - Applying what seems to be standard practice (but seems like cheating) where you end the episode when a life is lost in the game (but don't reset the environment). 91 | -------------------------------------------------------------------------------- /running_mean_std.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copied (and slightly modified) from OpenAI Baselines 3 | """ 4 | import numpy as np 5 | 6 | 7 | def apply_normalizer(data, normalizer, update_data=None, center=True, 8 | clip_limit=10): 9 | """Apply a RunningMeanStd normalizer to an array.""" 10 | if update_data is not None: 11 | # Update the statistics with different data than we're normalizing 12 | normalizer.update(update_data.reshape((-1, ) + normalizer.shape)) 13 | else: 14 | normalizer.update(data.reshape((-1, ) + normalizer.shape)) 15 | if center: 16 | data = data - normalizer.mean 17 | data = data / np.sqrt(normalizer.var + 1e-8) 18 | data = np.clip(data, -clip_limit, clip_limit) 19 | return data 20 | 21 | class RunningMeanStd(object): 22 | # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm 23 | def __init__(self, epsilon=1e-4, shape=()): 24 | self.mean = np.zeros(shape, 'float64') 25 | self.var = np.ones(shape, 'float64') 26 | self.count = epsilon 27 | self.shape = shape 28 | 29 | def update(self, x): 30 | """x must have shape (-1, self.shape[0], self.shape[1], etc)""" 31 | assert x.shape[1:] == self.shape, (x.shape, self.shape) 32 | batch_mean = np.mean(x, axis=0) 33 | batch_var = np.var(x, axis=0) 34 | batch_count = x.shape[0] 35 | self.update_from_moments(batch_mean, batch_var, batch_count) 36 | 37 | def update_from_moments(self, batch_mean, batch_var, batch_count): 38 | self.mean, self.var, self.count = update_mean_var_count_from_moments( 39 | self.mean, self.var, self.count, batch_mean, batch_var, batch_count) 40 | 41 | def update_mean_var_count_from_moments(mean, var, count, batch_mean, batch_var, batch_count): 42 | delta = batch_mean - mean 43 | tot_count = count + batch_count 44 | 45 | new_mean = mean + delta * batch_count / tot_count 46 | m_a = var * count 47 | m_b = batch_var * batch_count 48 | M2 = m_a + m_b + np.square(delta) * count * batch_count / tot_count 49 | new_var = M2 / tot_count 50 | new_count = tot_count 51 | 52 | return new_mean, new_var, new_count 53 | -------------------------------------------------------------------------------- /tracker.py: -------------------------------------------------------------------------------- 1 | import datetime 2 | import os 3 | import time 4 | 5 | import torch 6 | import wandb 7 | 8 | class WandBTracker: 9 | def __init__(self, name=None, args=None): 10 | wandb.init(project="pytorch-ppo", config=args) 11 | 12 | def add_histogram(self, tag, data, i): 13 | if type(data) == torch.Tensor: 14 | data = data.cpu().detach() 15 | try: 16 | wandb.log({tag: wandb.Histogram(data)}, step=i) 17 | except ValueError as e: 18 | # you get this eg if there's an inf in `value`, but also in some other cases 19 | print(f"got ValueError logging `{tag}` histogram to wandb: {e}") 20 | 21 | def add_scalar(self, tag, value, i): 22 | wandb.log({tag: value}, step=i) 23 | 24 | def add_image(self, tag, value, i): 25 | wandb.log({tag: [wandb.Image(value, caption="Label")]}, step=i) 26 | 27 | def log_iteration_time(self, batch_size, i): 28 | """Call this once per training iteration.""" 29 | try: 30 | dt = time.time() - self.last_time #noqa 31 | self.last_time = time.time() 32 | if i % 10 == 0: 33 | self.add_scalar("timings/iterations-per-sec", 1/dt, i) 34 | self.add_scalar("timings/samples-per-sec", batch_size/dt, i) 35 | except AttributeError: 36 | self.last_time = time.time() 37 | 38 | 39 | class ConsoleTracker: 40 | def __init__(self, name=None, args=None): 41 | pass 42 | 43 | def add_histogram(self, tag, data, i): 44 | pass 45 | 46 | def add_scalar(self, tag, value, i): 47 | print(f"{i} {tag}: {value}") 48 | 49 | def add_image(self, tag, value, i): 50 | pass 51 | 52 | def log_iteration_time(self, batch_size, i): 53 | """Call this once per training iteration.""" 54 | try: 55 | dt = time.time() - self.last_time #noqa 56 | self.last_time = time.time() 57 | if i % 10 == 0: 58 | print(f"{i} iterations-per-sec: {1/dt}") 59 | print(f"{i} samples-per-sec: {batch_size/dt}") 60 | except AttributeError: 61 | self.last_time = time.time() 62 | 63 | class NoTracker: 64 | def __init__(self, name=None, args=None): 65 | pass 66 | 67 | def add_histogram(self, tag, data, i): 68 | pass 69 | 70 | def add_scalar(self, tag, value, i): 71 | pass 72 | 73 | def add_image(self, tag, value, i): 74 | pass 75 | 76 | def log_iteration_time(self, batch_size, i): 77 | pass 78 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | def train_step(model, optim, batch_data, args, i, tracker): 5 | model.train() 6 | optim.zero_grad() 7 | 8 | advantages, rewards_to_go, values, actions, obs, \ 9 | selected_prob = batch_data 10 | 11 | values_new, dist_new = model(obs) 12 | values_new = values_new.flatten() 13 | selected_prob_new = dist_new.log_prob(actions) 14 | 15 | # Compute the PPO loss 16 | prob_ratio = torch.exp(selected_prob_new) / torch.exp(selected_prob) 17 | 18 | a = prob_ratio * advantages 19 | b = torch.clamp(prob_ratio, 1 - args.epsilon, 1 + args.epsilon) * advantages 20 | ppo_loss = -1 * torch.mean(torch.min(a, b)) 21 | 22 | # Compute the value function loss 23 | # Clipped loss - same idea as PPO loss, don't allow value to move too 24 | # far from where it was previously 25 | value_pred_clipped = values + (values_new - values).clamp(-args.epsilon, args.epsilon) 26 | value_losses = (values_new - rewards_to_go) ** 2 27 | value_losses_clipped = (value_pred_clipped - rewards_to_go) ** 2 28 | value_loss = 0.5 * torch.max(value_losses, value_losses_clipped) 29 | value_loss = value_loss.mean() 30 | 31 | entropy_loss = torch.mean(dist_new.entropy()) 32 | 33 | loss = ppo_loss + args.value_loss_coef * value_loss - args.entropy_coef * entropy_loss 34 | loss.backward() 35 | torch.nn.utils.clip_grad_norm_(model.parameters(), .5) 36 | optim.step() 37 | 38 | if i % 5 == 0: 39 | tracker.add_scalar("loss/ppo loss", ppo_loss, i) 40 | tracker.add_scalar("loss/value_loss", value_loss*args.value_loss_coef, i) 41 | tracker.add_scalar("loss/entropy_loss", -1*entropy_loss*args.entropy_coef, i) 42 | tracker.add_scalar("loss/loss", loss, i) 43 | tracker.add_scalar("training/mean value", torch.mean(values_new), i) 44 | tracker.add_scalar("training/mean rewards to go", torch.mean(rewards_to_go), i) 45 | tracker.add_scalar("training/mean prob ratio", torch.mean(prob_ratio), i) 46 | tracker.add_scalar("policy/policy entropy", entropy_loss, i) 47 | 48 | if i % 25 == 0: 49 | if len(actions.shape) == 2: 50 | for k in range(actions.shape[1]): 51 | tracker.add_histogram(f"policy/actions_{k}", actions[:, k], i) 52 | else: 53 | tracker.add_histogram("policy/actions", actions, i) 54 | 55 | tracker.add_histogram("training/values", values_new, i) 56 | tracker.add_histogram("training/advantages", advantages, i) 57 | tracker.add_histogram("training/rewards to go", rewards_to_go, i) 58 | tracker.add_histogram("training/prob ratio", prob_ratio, i) 59 | tracker.add_histogram("loss/ppo_loss_hist", torch.min(a, b), i) 60 | try: 61 | tracker.add_histogram("policy/var", model.dist.stds.exp().detach().cpu(), i) 62 | except AttributeError: 63 | pass 64 | -------------------------------------------------------------------------------- /util.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import gymnasium as gym 3 | 4 | def get_gym_env_info(env_name): 5 | env = gym.make(env_name) 6 | obs_shape = env.observation_space.shape 7 | num_obs = int(np.prod(obs_shape)) 8 | try: 9 | # discrete space 10 | num_actions = env.action_space.n 11 | action_type = "discrete" 12 | except AttributeError: 13 | # continuous space 14 | num_actions = env.action_space.shape[0] 15 | action_type = "continuous" 16 | return num_actions, obs_shape, num_obs, action_type 17 | 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size = 20732825, upload_time = "2025-04-22T21:50:38.317Z" }, 1036 | ] 1037 | -------------------------------------------------------------------------------- /worker.py: -------------------------------------------------------------------------------- 1 | from collections import deque 2 | import multiprocessing as mp 3 | 4 | import gymnasium as gym 5 | import numpy as np 6 | import torch 7 | from torchvision import transforms 8 | 9 | from running_mean_std import apply_normalizer 10 | 11 | class GamePlayer: 12 | """A manager class for running multiple game-playing processes.""" 13 | def __init__(self, args, shared_obs): 14 | self.episode_length = deque(maxlen=100) 15 | self.episode_rewards = deque(maxlen=100) 16 | 17 | # Start game-playing processes 18 | self.processes = [] 19 | for i in range(args.num_workers): 20 | parent_conn, child_conn = mp.Pipe() 21 | worker = SubprocWorker(i, child_conn, args, shared_obs) 22 | ps = mp.Process(target=worker.run) 23 | ps.start() 24 | self.processes.append((ps, parent_conn)) 25 | 26 | def run_rollout(self, args, shared_obs, rewards, discounted_rewards, values, 27 | policy_probs, actions, model, obs_normalizer, device, 28 | episode_ends): 29 | model.eval() 30 | # Start with the actions selected at the end of the previous iteration 31 | step_actions = actions[:, -1] 32 | # Same with obs 33 | shared_obs[:, 0] = shared_obs[:, -1] 34 | for step in range(args.num_steps): 35 | # Apply normalization to all steps except the first (which was 36 | # already normalized) 37 | obs = shared_obs[:, step] 38 | if step != 0: 39 | if len(obs.shape) == 2: 40 | obs = apply_normalizer(obs, obs_normalizer) 41 | shared_obs[:, step] = obs 42 | 43 | # run the model 44 | obs_torch = torch.tensor(obs).to(device).float() 45 | step_values, dist = model(obs_torch) 46 | 47 | # Sample actions from the policy distribution 48 | step_actions = dist.sample() 49 | step_policy_probs = dist.log_prob(step_actions) 50 | 51 | # Store data for use in training 52 | step_actions = step_actions.detach().cpu().numpy() 53 | values[:, step] = step_values.detach().cpu().numpy().flatten() 54 | policy_probs[:, step] = step_policy_probs.detach().cpu().numpy() 55 | actions[:, step] = step_actions 56 | 57 | # Send the selected actions to workers and request a step 58 | for j, (p, pipe) in enumerate(self.processes): 59 | pipe.send(("step", step, step_actions[j])) 60 | 61 | # Receive step data from workers 62 | for j, (p, pipe) in enumerate(self.processes): 63 | (reward, discounted_reward, done, info) = pipe.recv() 64 | rewards[j, step] = reward 65 | discounted_rewards[j, step] = discounted_reward 66 | episode_ends[j, step] = done 67 | try: 68 | self.episode_length.append(info['final_episode_length']) 69 | self.episode_rewards.append(info['final_episode_rewards']) 70 | except KeyError: 71 | pass 72 | 73 | 74 | class SubprocWorker: 75 | """A worker for running an environment, intended to be run on a separate 76 | process.""" 77 | def __init__(self, index, pipe, args, shared_obs): 78 | self.index = index 79 | self.pipe = pipe 80 | self.episode_steps = 0 81 | self.episode_rewards = 0 82 | self.disc_ep_rewards = 0 83 | self.previous_lives = 0 84 | self.args = args 85 | self.shared_obs = shared_obs 86 | 87 | # Data preprocessing for raw Atari frames 88 | self.transform = transforms.Compose([ 89 | transforms.ToPILImage(), 90 | transforms.Grayscale(), 91 | transforms.Resize((84, 84)), 92 | # Converts to tensor and from [0,255] to [0,1] 93 | transforms.ToTensor(), 94 | # For a tensor in range (0, 1), this will convert to range (-1, 1) 95 | # transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), 96 | ]) 97 | 98 | def run(self): 99 | """The worker entrypoint, will wait for commands from the main 100 | process and execute them.""" 101 | try: 102 | self.env = gym.make(self.args.env_name) 103 | self.env.reset() 104 | while True: 105 | cmd, t, action = self.pipe.recv() 106 | if cmd == 'step': 107 | self.pipe.send(self.step(action, t)) 108 | elif cmd == 'close': 109 | self.pipe.send(None) 110 | break 111 | else: 112 | raise RuntimeError('Got unrecognized cmd %s' % cmd) 113 | except KeyboardInterrupt: 114 | print('worker: got KeyboardInterrupt') 115 | finally: 116 | self.env.close() 117 | 118 | def step(self, action, t): 119 | """Perform a single step of the environment.""" 120 | info = {} 121 | step_reward = 0 122 | # We have the option to skip steps_to_skip steps, selecting the same 123 | # action this many times and returning the cumulative rewards 124 | # from the skipped steps and the final observation 125 | for _ in range(self.args.steps_to_skip): 126 | obs, reward, terminated, truncated, _ = self.env.step(action) 127 | done = terminated or truncated 128 | fake_done = done 129 | 130 | # DQN used this "cheat" where we check if the agent lost 131 | # a life, and if so we indicate the episode to be over 132 | # (but don't actually reset the environment unless it 133 | # really is over) 134 | if self.args.end_on_life_loss: 135 | lives = self.env.unwrapped.ale.lives() 136 | if (self.previous_lives > lives and lives > 0): 137 | # We died 138 | fake_done = True 139 | self.previous_lives = lives 140 | 141 | self.episode_rewards += reward 142 | step_reward += reward 143 | 144 | if done: 145 | info["final_episode_length"] = self.episode_steps 146 | info["final_episode_rewards"] = self.episode_rewards 147 | obs, _ = self.env.reset() 148 | 149 | # This breaks the Box2d games but should try adding it back for 150 | # Atari. 151 | # # perform a number of random steps after reset 152 | # for _ in range(np.random.randint(0, 30)): 153 | # obs, _, _done, _ = self.env.step(action) 154 | # if _done: 155 | # obs = self.env.reset() 156 | 157 | self.episode_steps = 0 158 | self.episode_rewards = 0 159 | 160 | self.episode_steps += 1 161 | 162 | # We store the observation in t+1 because it's really the next 163 | # step's observation 164 | if t < self.args.num_steps - 1: 165 | if self.args.model == "cnn": 166 | obs = self.transform(obs).numpy() 167 | self.shared_obs[self.index, t+1] = obs 168 | 169 | if done or fake_done: 170 | # Stop skipping steps and just finish this step 171 | break 172 | 173 | if self.args.clip_rewards: 174 | # clip reward to one of {-1, 0, 1} 175 | step_reward = np.sign(step_reward) 176 | 177 | self.disc_ep_rewards = self.disc_ep_rewards * self.args.gamma \ 178 | + step_reward 179 | last_disc_reward = self.disc_ep_rewards 180 | if done or fake_done: 181 | self.disc_ep_rewards = 0 182 | 183 | return step_reward, last_disc_reward, fake_done, info 184 | --------------------------------------------------------------------------------