Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning
Integrating expressive generative policies, such as flow-matching models, into offline reinforcement learning (RL) allows agents to capture complex, multi-modal behaviors. While Q-learning with Adjoint Matching (QAM) stabilizes policy optimization via the continuous adjoint method, it remains inherently bound to the fixed behavior distribution. This dependence induces a \textit{popularity bias} that can suppress high-reward actions in low-density regions, and creates a \textit{support binding} that restricts off-manifold exploration. Existing workarounds, such as appending \textit{residual} Gaussian policies, often re-introduce the expressivity bottlenecks associated with unimodal distrib...
| Authors | Abdelghani Ghanem, Mounir Ghogho |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 8 May 2026 |
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