Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach
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Computer Science > Machine Learning
Title:Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach
Abstract:We study the transfer of rewards learned using inverse reinforcement learning from expert demonstrations in one environment to reinforcement learning in a new, different environment. This arises naturally when demonstrations are collected in a controlled environment. We formulate the problem as a joint system of Bellman equations across the source and target environments and develop minimax estimators for the target soft-$q$-function. Whereas a sequential solution approach first estimates the source reward and then plugs it into the target control problem, a coupled approach solves the source and target system of equations jointly. We show that, in contrast to the sequential approach, the coupled approach removes the first-order influence of source Bellman residual error. We characterize the local behavior of each approach, develop finite-sample soft-$q$-function error bounds, and prove regret guarantees for the resulting soft-control policy. An empirical investigation using a sepsis simulator validates the theoretical comparison.
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.27834 [cs.LG] |
| (or arXiv:2605.27834v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27834
arXiv-issued DOI via DataCite (pending registration)
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