Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning
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arXiv
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866910463207407616 |
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| author | Town, Jared Morrison, Zachary Kamalapurkar, Rushikesh |
| author_facet | Town, Jared Morrison, Zachary Kamalapurkar, Rushikesh |
| contents | A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions. Nonuniqueness necessitates the study of the notion of equivalent solutions, i.e., solutions that result in a different cost functional but same feedback matrix, and convergence to such solutions. While offline algorithms that result in convergence to equivalent solutions have been developed in the literature, online, real-time techniques that address nonuniqueness are not available. In this paper, a regularized history stack observer that converges to approximately equivalent solutions of the IRL problem is developed. Novel data-richness conditions are developed to facilitate the analysis and simulation results are provided to demonstrate the effectiveness of the developed technique. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2210_16299 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning Town, Jared Morrison, Zachary Kamalapurkar, Rushikesh Systems and Control Machine Learning A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions. Nonuniqueness necessitates the study of the notion of equivalent solutions, i.e., solutions that result in a different cost functional but same feedback matrix, and convergence to such solutions. While offline algorithms that result in convergence to equivalent solutions have been developed in the literature, online, real-time techniques that address nonuniqueness are not available. In this paper, a regularized history stack observer that converges to approximately equivalent solutions of the IRL problem is developed. Novel data-richness conditions are developed to facilitate the analysis and simulation results are provided to demonstrate the effectiveness of the developed technique. |
| title | Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2210.16299 |