Metadata-Version: 2.1
Name: pernaf
Version: 0.0.14
Summary: An implementation of the Normalized Advantage Function Reinforcement Learning Algorithm with Prioritized Experience Replay
Home-page: https://github.com/MathPhysSim/PER-NAF
Author: Simon Hirlaender
Author-email: simon.hirlaender@cern.ch
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: tensorflow (==1.14)
Requires-Dist: numpy
Requires-Dist: gym

# PER-NAF
An implementation of the Normalized Advantage Function Reinforcement Learning Algorithm with Prioritized Experience Replay

## Summary
* The original paper of this code is: https://arxiv.org/abs/1509.02971
* The code is mainly based on: https://github.com/carpedm20/NAF-tensorflow/
* Additionally I added the prioritized experience replay: https://arxiv.org/abs/1511.05952
* Using the OpenAI baseline implementation: https://github.com/openai/baselines/blob/master/baselines/deepq/replay_buffer.py

Thanks openAI and Kim!


## Some Advices from experience in RL

* Normalize the state and action space as well as the reward is a good practice
* Visualise as much as possible to get an intuition about the method as possible bugs
* If it does not make sense it is a bug with very high probability



> Coding makes happy 






