takuseno/ppo

Proximal Policy Optimization implementation with TensorFlow

PPO

Proximal Policy Optimization implementation with Tensorflow.

https://arxiv.org/pdf/1707.06347.pdf

This repository has been much updated from commit id a4fbd383f0f89ce2d881a8b78d6b8a03294e5c7c . New PPO requires a new dependency, rlsaber which is my utility repository that can be shared across different algorithms.

Some of my design follow OpenAI baselines. But, I used as many default tensorflow packages as possible unlike baselines, that makes my codes easier to be read.

In addition, my PPO automatically switches between continuous action-space and discrete action-space depending on environments. If you want to change hyper parameters, check atari_constants.py or box_constants.py, which will be loaded depending on environments too.

requirements

• Python3

dependencies

• tensorflow
• gym[atari]
• opencv-python
• git+https://github.com/imai-laboratory/rlsaber

usage

training

$ python train.py [--env env-id] [--render] [--logdir log-name]

example

$ python train.py --env BreakoutNoFrameskip-v4 --logdir breakout

playing

$ python train.py --demo --load results/path-to-model [--env env-id] [--render]

example

$ python train.py --demo --load results/breakout/model.ckpt-xxxx --env BreakoutNoFrameskip-v4 --render

performance examples

Pendulumn-v0

BreakoutNoFrameskip-v4

implementation

This is inspired by following projects.

• DQN
• OpenAI Baselines

License

This repository is MIT-licensed.