takuseno / Ppo
Licence: mit
Proximal Policy Optimization implementation with TensorFlow
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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.
License
This repository is MIT-licensed.
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