pytorch-a2c-ppo-acktr/README.md

pytorch-a2c-ppo-acktr

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.

TODO

• Add MuJoCo and continuous actions
• Improve performance of KFAC, see kfac.py for more information
• Run evaluation for all games and algorithms

Usage

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

Results

A2C

PPO

Coming soon.

ACKTR

Coming soon.