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
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}},
}