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https://github.com/kartik2309/rlpack

Implementation of RL Algorithms with PyTorch.
https://github.com/kartik2309/rlpack

a2c-agent a2c-algorithm a2c-pytorch a3c a3c-agent a3c-pytorch deep-reinforcement-learning dqn dqn-pytorch gym-environments prioritized-experience-replay pytorch-implementation reinforcement-learning

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Implementation of RL Algorithms with PyTorch.

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README

          

# RLPack

# Introduction

RLPack; Reinforcement Learning Package is the implementation of RL Algorithms built on top of PyTorch. Heavy
workloads have been optimized with C++ backend with CUDA and OpenMP (depending on the hardware availability).

# Installation

#### To install RLPack, you can simply do

```bash
python -m pip install -U pip
pip install RLPack
```

#### To build and install RLPack from source, simply clone and install as follows:

```bash
python -m pip install -U pip
git clone https://github.com/kartik2309/RLPack.git
cd RLPack
pip install . --use-feature=in-tree-build
```
This will install the package in your python environment.

# Documentation
RLPack documentation was generated by Doxygen. It can be referred [here](https://kartik2309.github.io/RLPack/html/index.html)

# Issues and bugs
Please raise an issue if you encounter a bug. If you would like to fix the bug and contribute to RLPack, kindly refer
to [CONTRIBUTING.md](https://github.com/kartik2309/RLPack/blob/master/CONTRIBUTING.md)

# License
RLPack is released under [MIT LICENSE](https://github.com/kartik2309/RLPack/blob/master/LICENSE.md).