Flow-Based Policy for Online Reinforcement Learning

• URL: http://arxiv.org/abs/2506.12811v1
• Date: Sun, 15 Jun 2025 10:53:35 GMT
• Title: Flow-Based Policy for Online Reinforcement Learning
• Authors: Lei Lv, Yunfei Li, Yu Luo, Fuchun Sun, Tao Kong, Jiafeng Xu, Xiao Ma,
• Abstract summary: FlowRL is a framework for online reinforcement learning that integrates flow-based policy representation with Wasserstein-2-regularized optimization.<n>We show that FlowRL achieves competitive performance in online reinforcement learning benchmarks.
• Score: 34.86742824686496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present \textbf{FlowRL}, a novel framework for online reinforcement learning that integrates flow-based policy representation with Wasserstein-2-regularized optimization. We argue that in addition to training signals, enhancing the expressiveness of the policy class is crucial for the performance gains in RL. Flow-based generative models offer such potential, excelling at capturing complex, multimodal action distributions. However, their direct application in online RL is challenging due to a fundamental objective mismatch: standard flow training optimizes for static data imitation, while RL requires value-based policy optimization through a dynamic buffer, leading to difficult optimization landscapes. FlowRL first models policies via a state-dependent velocity field, generating actions through deterministic ODE integration from noise. We derive a constrained policy search objective that jointly maximizes Q through the flow policy while bounding the Wasserstein-2 distance to a behavior-optimal policy implicitly derived from the replay buffer. This formulation effectively aligns the flow optimization with the RL objective, enabling efficient and value-aware policy learning despite the complexity of the policy class. Empirical evaluations on DMControl and Humanoidbench demonstrate that FlowRL achieves competitive performance in online reinforcement learning benchmarks.

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