Bridging Dynamics Gaps via Diffusion Schrödinger Bridge for Cross-Domain Reinforcement Learning

• URL: http://arxiv.org/abs/2602.23737v1
• Date: Fri, 27 Feb 2026 07:04:22 GMT
• Title: Bridging Dynamics Gaps via Diffusion Schrödinger Bridge for Cross-Domain Reinforcement Learning
• Authors: Hanping Zhang, Yuhong Guo,
• Abstract summary: Cross-domain reinforcement learning (RL) aims to learn transferable policies under dynamics shifts between source and target domains.<n>A key challenge lies in the lack of target-domain environment interaction and reward supervision, which prevents direct policy learning.<n>We propose Bridging Dynamics Gaps for Cross-Domain Reinforcement Learning (BDGxRL) to align source transitions with target-domain dynamics encoded in offline demonstrations.
• Score: 23.628360655654507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain reinforcement learning (RL) aims to learn transferable policies under dynamics shifts between source and target domains. A key challenge lies in the lack of target-domain environment interaction and reward supervision, which prevents direct policy learning. To address this challenge, we propose Bridging Dynamics Gaps for Cross-Domain Reinforcement Learning (BDGxRL), a novel framework that leverages Diffusion Schrödinger Bridge (DSB) to align source transitions with target-domain dynamics encoded in offline demonstrations. Moreover, we introduce a reward modulation mechanism that estimates rewards based on state transitions, applying to DSB-aligned samples to ensure consistency between rewards and target-domain dynamics. BDGxRL performs target-oriented policy learning entirely within the source domain, without access to the target environment or its rewards. Experiments on MuJoCo cross-domain benchmarks demonstrate that BDGxRL outperforms state-of-the-art baselines and shows strong adaptability under transition dynamics shifts.

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