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https://github.com/wassname/pytorch-a2c-ppo-acktr.git
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Ilya Kostrikov
86 lines
2.9 KiB
Markdown
86 lines
2.9 KiB
Markdown
# pytorch-a2c-ppo-acktr
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## Update 09/27/2017: now supports both Atari and MuJoCo/Roboschool!
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This is a PyTorch implementation of
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* Advantage Actor Critic (A2C), a synchronous deterministic version of [A3C](https://arxiv.org/pdf/1602.01783v1.pdf)
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* Proximal Policy Optimization [PPO](https://arxiv.org/pdf/1707.06347.pdf)
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* Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation [ACKTR](https://arxiv.org/abs/1708.05144)
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Also see the OpenAI posts: [A2C/ACKTR](https://blog.openai.com/baselines-acktr-a2c/) and [PPO](https://blog.openai.com/openai-baselines-ppo/) for more information.
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This implementation is inspired by the OpenAI baselines for [A2C](https://github.com/openai/baselines/tree/master/baselines/a2c), [ACKTR](https://github.com/openai/baselines/tree/master/baselines/acktr) and [PPO](https://github.com/openai/baselines/tree/master/baselines/ppo1). It uses the same hyper parameters and the model since they were well tuned for Atari games.
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## Contributions
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Contributions are very welcome. If you know how to make this code better, don't hesitate to send a pull request. Also see a todo list below.
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Also I'm searching for volunteers to run all experiments on Atari and MuJoCo (with multiple random seeds).
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## Disclaimer
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It's extremely difficult to reproduce results for Reinforcement Learning methods. See ["Deep Reinforcement Learning that Matters"](https://arxiv.org/abs/1709.06560) for more information. I tried to reproduce OpenAI results as closely as possible. However, majors differences in performance can be caused even by minor differences in TensorFlow and PyTorch libraries.
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### TODO
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* Improve this README file. Rearrange images.
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* Improve performance of KFAC, see kfac.py for more information
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* Run evaluation for all games and algorithms
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## Usage
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### Atari
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#### A2C
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```
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python main.py --env-name "PongNoFrameskip-v4"
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```
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#### PPO
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```
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python main.py --env-name "PongNoFrameskip-v4" --algo ppo --use-gae --num-processes 8 --num-steps 256 --vis-interval 1 --log-interval 1
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```
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#### ACKTR
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```
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python main.py --env-name "PongNoFrameskip-v4" --algo acktr --num-processes 32 --num-steps 20
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```
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### MuJoCo
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#### A2C
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```
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python main.py --env-name "Reacher-v1" --num-stack 1 --num-frames 1000000
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```
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#### PPO
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```
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python main.py --env-name "Reacher-v1" --algo ppo --use-gae --vis-interval 1 --log-interval 1 --num-stack 1 --num-steps 2048 --num-processes 1 --lr 3e-4 --entropy-coef 0 --ppo-epoch 10 --batch-size 64 --gamma 0.99 --tau 0.95 --num-frames 1000000
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```
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#### ACKTR
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ACKTR requires some modifications to be made specifically for MuJoCo. But at the moment, I want to keep this code as unified as possible. Thus, I'm going for better ways to integrate it into the codebase.
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## Results
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### A2C
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### PPO
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Coming soon.
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### ACKTR
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Coming soon.
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