Exploiting Symmetry in Dynamics for Model-Based Reinforcement Learning with Asymmetric Rewards
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916360161853440 |
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| author | Sonmez, Yasin Junnarkar, Neelay Arcak, Murat |
| author_facet | Sonmez, Yasin Junnarkar, Neelay Arcak, Murat |
| contents | Recent work in reinforcement learning has leveraged symmetries in the model to improve sample efficiency in training a policy. A commonly used simplifying assumption is that the dynamics and reward both exhibit the same symmetry; however, in many real-world environments, the dynamical model exhibits symmetry independent of the reward model. In this paper, we assume only the dynamics exhibit symmetry, extending the scope of problems in reinforcement learning and learning in control theory to which symmetry techniques can be applied. We use Cartan's moving frame method to introduce a technique for learning dynamics that, by construction, exhibit specified symmetries. Numerical experiments demonstrate that the proposed method learns a more accurate dynamical model |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_19024 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploiting Symmetry in Dynamics for Model-Based Reinforcement Learning with Asymmetric Rewards Sonmez, Yasin Junnarkar, Neelay Arcak, Murat Machine Learning Artificial Intelligence Robotics Systems and Control Recent work in reinforcement learning has leveraged symmetries in the model to improve sample efficiency in training a policy. A commonly used simplifying assumption is that the dynamics and reward both exhibit the same symmetry; however, in many real-world environments, the dynamical model exhibits symmetry independent of the reward model. In this paper, we assume only the dynamics exhibit symmetry, extending the scope of problems in reinforcement learning and learning in control theory to which symmetry techniques can be applied. We use Cartan's moving frame method to introduce a technique for learning dynamics that, by construction, exhibit specified symmetries. Numerical experiments demonstrate that the proposed method learns a more accurate dynamical model |
| title | Exploiting Symmetry in Dynamics for Model-Based Reinforcement Learning with Asymmetric Rewards |
| topic | Machine Learning Artificial Intelligence Robotics Systems and Control |
| url | https://arxiv.org/abs/2403.19024 |