rlib 1.0.0

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

Uses Tensorflow v1.14 as the framework for training the neural networks models used by the RL agents.

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