Offline inverse reinforcement learning (IRL) aims to recover a reward function that explains expert behavior using only fixed demonstration data, without any additional online interaction. We propose BiCQL-ML, a policy-free offline IRL algorithm that jointly optimizes a reward function and a conservative Q-function in a bi-level framework, thereby avoiding explicit policy learning. The method alternates between (i) learning a conservative Q-function via Conservative Q-Learning (CQL) under the current reward, and (ii) updating the reward parameters to maximize the expected Q-values of expert actions while suppressing over-generalization to out-of-distribution actions. This procedure can be viewed as maximum likelihood estimation under a soft value matching principle. We provide theoretical guarantees that BiCQL-ML converges to a reward function under which the expert policy is soft-optimal. Empirically, we show on standard offline RL benchmarks that BiCQL-ML improves both reward recovery and downstream policy performance compared to existing offline IRL baselines.

翻译：离线逆强化学习（IRL）旨在仅利用固定的演示数据恢复解释专家行为的奖励函数，无需任何额外的在线交互。我们提出BiCQL-ML，一种无策略的离线IRL算法，在双层框架中联合优化奖励函数和保守Q函数，从而避免显式的策略学习。该方法交替进行以下步骤：（i）在当前奖励下通过保守Q学习（CQL）学习保守Q函数；（ii）更新奖励参数以最大化专家行动的期望Q值，同时抑制对分布外行动的过度泛化。此过程可视为软值匹配原则下的最大似然估计。我们提供了理论保证，证明BiCQL-ML会收敛到一个奖励函数，在该函数下专家策略是软最优的。实证研究表明，在标准离线强化学习基准测试中，与现有离线IRL基线相比，BiCQL-ML在奖励恢复和下游策略性能方面均有提升。