A small Pytorch based reinforcement learning library
as used for my MSc dissertation project 'Dealing with sparse rewards in reinforcement learning'.

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

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

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

@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}},
}