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relational_deep_reinforcement_learning's Introduction

Implementation of Relational Deep Reinforcement Learning

This Repository is implementation of Relational Deep Reinforcement Learning to Breakout Environment.

The Reinforcement Learning Algorithm is Proximal Policy Optimization

Configuration

  • This paper requires heavy computation power.
  • Left Figure is the map of attention which is produced by self-attention.
  • Though the paper developed 100 environments for experiment, the implementer of this repository created only 16 environments with the limitation of computer resources. So sometimes it's exactly the performance and sometimes it's not.
  • If you want to see more significant attention map, just control CNN function to have less strides and more filters. In this repository, 84, 84 images are processed to have 19, 19 because of my computation limit.

Initial Training status

During Training

Tensorboard

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relational_deep_reinforcement_learning's Issues

Computational resources

Hi, great work! I'm curious as to how much compute (in terms of # cpu cores, # and type of gpus) it takes to run the example on breakout. Thanks!

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