juxiao/pytorch-a2c-ppo-acktr

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO) and Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR).

pytorch-a2c-ppo-acktr

Update 10/06/2017: added enjoy.py and a link to pretrained models!

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.

Supported (and tested) environments (via OpenAI Gym)

• Atari Learning Environment
• MuJoCo
• PyBullet (including Racecar, Minitaur and Kuka)

I highly recommend PyBullet as a free open source alternative to MuJoCo for continuous control tasks.

All environments are operated using exactly the same Gym interface. See their documentations for a comprehensive list.

Requirements

• Python 3 (it might work with Python 2, but I didn't test it)
• PyTorch
• Visdom
• OpenAI baselines

In order to install requirements, follow:

# PyTorch
conda install pytorch torchvision -c soumith

# Baselines for Atari preprocessing
git clone https://github.com/openai/baselines.git
cd baselines
pip install -e .

# Other requirements
pip install -r requirements.txt

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

Training

Start a Visdom server with python -m visdom.server, it will serve http://localhost:8097/ by default.

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.

Enjoy

Load a pretrained model from my Google Drive.

Disclaimer: I might have used different hyper-parameters to train these models.

Atari

python enjoy.py --load-dir trained_models/a2c --env-name "PongNoFrameskip-v4" --num-stack 4

MuJoCo

python enjoy.py --load-dir trained_models/ppo --env-name "Reacher-v1" --num-stack 1

Results

A2C

PPO

ACKTR