Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach

• URL: http://arxiv.org/abs/2605.27834v1
• Date: Wed, 27 May 2026 01:46:20 GMT
• Title: Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach
• Abstract summary: 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.<n>We formulate the problem as a joint system of Bellman equations across the source and target environments.<n>We show that, in contrast to the sequential approach, the coupled approach removes the first-order influence of source Bellman residual error.
• Score: 38.74706512581043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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