Keywords: Partial Observability, Equivariant Learning, Symmetry
TL;DR: This paper embeds domain symmetry in actor-critic reinforcement learning agents to solve a specific class of partially observable domains
Abstract: Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable domains where symmetries can be a useful inductive bias for efficient learning. Specifically, by encoding the equivariance regarding specific group symmetries into the neural networks, our actor-critic reinforcement learning agents can reuse solutions in the past for related scenarios. Consequently, our equivariant agents outperform non-equivariant approaches significantly in terms of sample efficiency and final performance, demonstrated through experiments on a range of robotic tasks in simulation and real hardware.
Student First Author: yes
Supplementary Material: zip
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Publication Agreement: pdf
Poster Spotlight Video: mp4