ipark-cs / reinforcement-learning

A small reinforcement learning library for my masters dissertation project

reinforcement-learning

A small reinforcement learning library used for MSc dissertation project 'Dealing with sparse rewards in reinforcement learning' at the University of Sheffield.

This repository has working implementations of the following reinforcement agents:
1. Advantage Actor Critic (A2C)
2. Synchronous n-step Double Deep Q Network (Sync-DDQN)
3. Proximal Policy Optimisation (PPO)
4. Random Network Distillation (RND)
5. UNREAL-A2C2, A2C-CNN version of the UNREAL agent
6. Random Network Distillation with Auxiliary Learning (RANDAL), novel solution combining UNREAL and RND agents

Install repository:

git clone https://github.com/jhare96/reinforcement-learning.git
pip install -e reinforcement-learning

To cite RANDAL agents in publications:

follow the link to the ArXiv publication https://arxiv.org/abs/1910.09281

To cite this repository in publications:

@misc{Hare_rlib,
  author = {Joshua Hare},
  title = {reinforcement learning library, rlib},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/jhare96/reinforcement-learning}},
}