Group Equivariant Deep Reinforcement Learning
Abstract
In Reinforcement Learning (RL), Convolutional Neural Networks(CNNs) have been successfully applied as function approximators in Deep Q-Learning algorithms, which seek to learn action-value functions and policies in various environments. However, to date, there has been little work on the learning of symmetry-transformation equivariant representations of the input environment state. In this paper, we propose the use of Equivariant CNNs to train RL agents and study their inductive bias for transformation equivariant Q-value approximation. We demonstrate that equivariant architectures can dramatically enhance the performance and sample efficiency of RL agents in a highly symmetric environment while requiring fewer parameters. Additionally, we show that they are robust to changes in the environment caused by affine transformations.
Cite
@article{arxiv.2007.03437,
title = {Group Equivariant Deep Reinforcement Learning},
author = {Arnab Kumar Mondal and Pratheeksha Nair and Kaleem Siddiqi},
journal= {arXiv preprint arXiv:2007.03437},
year = {2020}
}
Comments
Presented at the ICML 2020 Workshop on Inductive Biases, Invariances and Generalization in RL