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arXiv · 2408.14336

Equivariant Reinforcement Learning under Partial Observability

Hai Nguyen·Andrea Baisero·David Klee·Dian Wang·Robert Platt·Christopher Amato
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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.

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Hai Nguyen, Andrea Baisero, David Klee, Dian Wang, Robert Platt, Christopher Amato. 2024-08-26. Equivariant Reinforcement Learning under Partial Observability. https://arxiv.org/abs/2408.14336

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