pytorch-a2c-ppo-acktr

Update 09/27/2017: now supports both Atari and MuJoCo/Roboschool!

This is a PyTorch implementation of

Advantage Actor Critic (A2C), a synchronous deterministic version of A3C
Proximal Policy Optimization PPO
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation ACKTR

Also see the OpenAI posts: A2C/ACKTR and PPO for more information.

This implementation is inspired by the OpenAI baselines for A2C, ACKTR and PPO. It uses the same hyper parameters and the model since they were well tuned for Atari games.

Contributions

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.

Also I'm searching for volunteers to run all experiments on Atari and MuJoCo (with multiple random seeds).

Disclaimer

It's extremely difficult to reproduce results for Reinforcement Learning methods. See "Deep Reinforcement Learning that Matters" 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.

TODO

Improve this README file. Rearrange images.
Improve performance of KFAC, see kfac.py for more information
Run evaluation for all games and algorithms

Usage

Atari

A2C

python main.py --env-name "PongNoFrameskip-v4"

PPO

python main.py --env-name "PongNoFrameskip-v4" --algo ppo --use-gae --num-processes 8 --num-steps 256 --vis-interval 1 --log-interval 1

ACKTR

python main.py --env-name "PongNoFrameskip-v4" --algo acktr --num-processes 32 --num-steps 20

MuJoCo

A2C

python main.py --env-name "Reacher-v1" --num-stack 1 --num-frames 1000000

PPO

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

ACKTR

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.