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This is a Quantitative Study study (also classified as: Experimental Study).

October 15, 2025Open Access

Flow-Based Policy for Online Reinforcement Learning

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FlowRL demonstrates competitive reinforcement learning performance, suggesting enhanced action distributions and effective Q-learning.

FlowRL aligns flow optimization with reinforcement learning objectives, enabling improved policy learning.
Empirical evaluations on DMControl and Humanoidbench show FlowRL achieves competitive benchmarks in online reinforcement learning.
The framework utilizes a state-dependent velocity field to model policies, generating actions from noise.
By bounding the Wasserstein-2 distance, FlowRL maintains proximity to an optimal behavior policy derived from the replay buffer.

Lv et al. (2025) studied this question.

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October 15, 2025Open Access

View Full Paper

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Authors

FlowRL demonstrates competitive reinforcement learning performance, suggesting enhanced action distributions and effective Q-learning.

FlowRL aligns flow optimization with reinforcement learning objectives, enabling improved policy learning.
Empirical evaluations on DMControl and Humanoidbench show FlowRL achieves competitive benchmarks in online reinforcement learning.
The framework utilizes a state-dependent velocity field to model policies, generating actions from noise.
By bounding the Wasserstein-2 distance, FlowRL maintains proximity to an optimal behavior policy derived from the replay buffer.

Lv et al. (2025) studied this question.

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