DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning
Researchers propose DADiff, a diffusion-driven method to adapt reinforcement‑learning policies across domains with limited target data.
- DADiff uses diffusion models to adapt RL policies across domains with limited target data.
- The approach outperforms existing domain‑classifier and representation‑learning baselines in experiments.
- It offers a new generative‑modeling perspective for tackling dynamics mismatch in reinforcement learning.
The paper addresses the challenge of transferring reinforcement‑learning policies when the source and target environments have mismatched dynamics. It focuses on online dynamics adaptation, where extensive training data exists for the source domain but only a few interactions are possible in the target domain.
Instead of traditional techniques such as domain classifiers or representation learning, the authors introduce a generative diffusion model that learns to modify the policy distribution to suit the target dynamics. Experiments demonstrate that DADiff can achieve better performance than baseline methods with fewer target interactions.
The work contributes a novel perspective by treating domain adaptation as a generative modeling problem, potentially opening new avenues for efficient policy transfer in robotics and simulation‑to‑real scenarios.
Provides a technique to reduce the amount of real‑world data needed when deploying RL agents to new environments.
Illustrates an emerging research direction combining diffusion models with reinforcement learning.
Shows a novel way to make AI agents more adaptable across different settings.
- diffusion model
- A generative model that progressively adds and removes noise to learn data distributions.
- domain adaptation
- Transferring a model trained in one environment to perform well in another with different dynamics.
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