FLAC: Maximum Entropy RL via Kinetic Energy Regularized Bridge Matching

• URL: http://arxiv.org/abs/2602.12829v1
• Date: Fri, 13 Feb 2026 11:32:10 GMT
• Title: FLAC: Maximum Entropy RL via Kinetic Energy Regularized Bridge Matching
• Authors: Lei Lv, Yunfei Li, Yu Luo, Fuchun Sun, Xiao Ma,
• Abstract summary: We propose a framework that regulates policyity by penalizing the kinetic energy of the velocity field.<n>We derive an energy-regularized policy scheme and a practical off-policy algorithm that automatically tunes the kinetic energy.
• Score: 28.98935867615678
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Iterative generative policies, such as diffusion models and flow matching, offer superior expressivity for continuous control but complicate Maximum Entropy Reinforcement Learning because their action log-densities are not directly accessible. To address this, we propose Field Least-Energy Actor-Critic (FLAC), a likelihood-free framework that regulates policy stochasticity by penalizing the kinetic energy of the velocity field. Our key insight is to formulate policy optimization as a Generalized Schrödinger Bridge (GSB) problem relative to a high-entropy reference process (e.g., uniform). Under this view, the maximum-entropy principle emerges naturally as staying close to a high-entropy reference while optimizing return, without requiring explicit action densities. In this framework, kinetic energy serves as a physically grounded proxy for divergence from the reference: minimizing path-space energy bounds the deviation of the induced terminal action distribution. Building on this view, we derive an energy-regularized policy iteration scheme and a practical off-policy algorithm that automatically tunes the kinetic energy via a Lagrangian dual mechanism. Empirically, FLAC achieves superior or comparable performance on high-dimensional benchmarks relative to strong baselines, while avoiding explicit density estimation.

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