Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach

ArXi:2605.27834v1 Announce Type: new We study the transfer of rewards learned using inverse reinforcement learning from expert nstrations in one environment to reinforcement learning in a new, different environment. This arises naturally when nstrations 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.