On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912728332894208 |
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| author | Freihaut, Till Ramponi, Giorgia |
| author_facet | Freihaut, Till Ramponi, Giorgia |
| contents | Multi-agent Inverse Reinforcement Learning (MAIRL) aims to recover agent reward functions from expert demonstrations. We characterize the feasible reward set in Markov games, identifying all reward functions that rationalize a given equilibrium. However, equilibrium-based observations are often ambiguous: a single Nash equilibrium can correspond to many reward structures, potentially changing the game's nature in multi-agent systems. We address this by introducing entropy-regularized Markov games, which yield a unique equilibrium while preserving strategic incentives. For this setting, we provide a sample complexity analysis detailing how errors affect learned policy performance. Our work establishes theoretical foundations and practical insights for MAIRL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15046 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning Freihaut, Till Ramponi, Giorgia Machine Learning Multi-agent Inverse Reinforcement Learning (MAIRL) aims to recover agent reward functions from expert demonstrations. We characterize the feasible reward set in Markov games, identifying all reward functions that rationalize a given equilibrium. However, equilibrium-based observations are often ambiguous: a single Nash equilibrium can correspond to many reward structures, potentially changing the game's nature in multi-agent systems. We address this by introducing entropy-regularized Markov games, which yield a unique equilibrium while preserving strategic incentives. For this setting, we provide a sample complexity analysis detailing how errors affect learned policy performance. Our work establishes theoretical foundations and practical insights for MAIRL. |
| title | On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.15046 |