Equivariant Reinforcement Learning under Partial Observability
<|reference_start|>Equivariant Reinforcement Learning under Partial Observability: 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.<|reference_end|>
@article{nguyen2024equivariant,
    title={Equivariant Reinforcement Learning under Partial Observability},
    author={Hai Nguyen, Andrea Baisero, David Klee, Dian Wang, Robert Platt,
  Christopher Amato},
    journal={arXiv preprint arXiv:2408.14336},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14336},
    primaryClass={cs.RO cs.AI cs.CV}
}