Equivariant Reinforcement Learning under Partial Observability

• URL: http://arxiv.org/abs/2408.14336v1
• Date: Mon, 26 Aug 2024 15:07:01 GMT
• Title: Equivariant Reinforcement Learning under Partial Observability
• Authors: Hai Nguyen, Andrea Baisero, David Klee, Dian Wang, Robert Platt, Christopher Amato,
• Abstract summary: This paper identifies partially observable domains where symmetries can be a useful inductive bias for efficient learning. Our actor-critic reinforcement learning agents can reuse solutions in the past for related scenarios.
• Score: 18.87759041528553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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