Equivariant Reinforcement Learning under Partial Observability
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909295946235904 |
|---|---|
| author | Nguyen, Hai Baisero, Andrea Klee, David Wang, Dian Platt, Robert Amato, Christopher |
| author_facet | Nguyen, Hai Baisero, Andrea Klee, David Wang, Dian Platt, Robert Amato, Christopher |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Equivariant Reinforcement Learning under Partial Observability Nguyen, Hai Baisero, Andrea Klee, David Wang, Dian Platt, Robert Amato, Christopher Robotics Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Equivariant Reinforcement Learning under Partial Observability |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.14336 |