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Bridging Dynamics Gaps via Diffusion Schr\"odinger Bridge for Cross-Domain Reinforcement Learning
🤖AI Summary
Researchers propose BDGxRL, a novel framework using Diffusion Schrödinger Bridge to enable reinforcement learning agents to transfer policies across different domains without direct target environment access. The method aligns source domain transitions with target dynamics through offline demonstrations and introduces reward modulation for consistent learning.
Key Takeaways
- →BDGxRL addresses cross-domain reinforcement learning challenges by bridging dynamics gaps between source and target environments.
- →The framework uses Diffusion Schrödinger Bridge to align source transitions with target-domain dynamics from offline demonstrations.
- →A reward modulation mechanism estimates rewards based on state transitions to ensure consistency with target-domain dynamics.
- →The method enables policy learning entirely within the source domain without requiring target environment access or rewards.
- →Experiments on MuJoCo benchmarks show BDGxRL outperforms existing baselines in handling transition dynamics shifts.
#reinforcement-learning#cross-domain#diffusion-models#transfer-learning#machine-learning#dynamics#policy-learning#mujoco#research
Read Original →via arXiv – CS AI
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