Bridging Dynamics Gaps via Diffusion Schr\"odinger Bridge for Cross-Domain Reinforcement Learning
arXiv:2602.23737v1 Announce Type: new 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 Schr\"odinger 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 BDG
arXiv:2602.23737v1 Announce Type: new 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 Schr\"odinger 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.
Executive Summary
This article presents BDGxRL, a novel framework for cross-domain reinforcement learning that leverages Diffusion Schrödinger Bridge (DSB) to align source transitions with target-domain dynamics. The framework introduces a reward modulation mechanism to ensure consistency between rewards and target-domain dynamics. Experiments on MuJoCo cross-domain benchmarks demonstrate that BDGxRL outperforms state-of-the-art baselines and shows strong adaptability under transition dynamics shifts. The framework enables target-oriented policy learning entirely within the source domain, without access to the target environment or its rewards. This approach has significant implications for real-world applications, particularly in scenarios where direct interaction with the target environment is not feasible or desirable.
Key Points
- ▸ BDGxRL leverages DSB to align source transitions with target-domain dynamics.
- ▸ The framework introduces a reward modulation mechanism to ensure consistency between rewards and target-domain dynamics.
- ▸ BDGxRL enables target-oriented policy learning entirely within the source domain, without access to the target environment or its rewards.
Merits
Strength in Adaptability
BDGxRL demonstrates strong adaptability under transition dynamics shifts, making it a robust framework for cross-domain reinforcement learning.
Improved Policy Learning
The framework enables target-oriented policy learning, which can lead to more accurate and effective policy learning in cross-domain scenarios.
Demerits
Limited Generalizability
The framework's performance may be limited to specific MuJoCo cross-domain benchmarks, and its generalizability to other domains or tasks is not well-established.
Computational Complexity
The use of DSB and reward modulation mechanisms may introduce significant computational complexity, which could be a barrier to practical implementation.
Expert Commentary
The article presents a novel and innovative approach to cross-domain reinforcement learning, which has the potential to address significant challenges in the field. However, the framework's limitations and computational complexity need to be carefully considered before practical implementation. Furthermore, the article highlights the importance of transfer learning and offline reinforcement learning in real-world applications.
Recommendations
- ✓ Further research should be conducted to investigate the generalizability of BDGxRL to other domains and tasks.
- ✓ The computational complexity of the framework should be optimized to make it more practical for real-world applications.