Group Equivariant Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2007.03437v1
• Date: Wed, 1 Jul 2020 02:38:48 GMT
• Title: Group Equivariant Deep Reinforcement Learning
• Authors: Arnab Kumar Mondal, Pratheeksha Nair, Kaleem Siddiqi
• Abstract summary: 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.
• Score: 4.997686360064921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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