Reinforcement Learning via Fixed-point Flow Matching
In maximum entropy and behavior-constrained reinforcement learning (RL), the optimal policy is given by a Boltzmann distribution. Generating samples from this distribution can be challenging, as it is often complex and multimodal. While flow policies trained with flow matching provide a powerful framework for representing such distributions, they traditionally rely on access to training data, which is generally unavailable in online RL. To address this limitation, we propose fixed-point flow matching (FFM), a data-free procedure for training and fine-tuning flow policies in RL. FFM leverages the target flow identity to formulate the actor update as a fixed-point iteration, utilizing a flo...
| Authors | Denis Blessing, Onur Celik, Gerhard Neumann |
|---|---|
| Primary category | cs.LG |
| Published | 9 October 2026 |
| Last updated at source | 14 October 2026 |
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