Summary of Learning From Random Demonstrations: Offline Reinforcement Learning with Importance-sampled Diffusion Models, by Zeyu Fang et al.
Learning from Random Demonstrations: Offline Reinforcement Learning with Importance-Sampled Diffusion Models
by Zeyu Fang, Tian Lan
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method for offline reinforcement learning with closed-loop policy evaluation and world-model adaptation leverages a guided diffusion world model to directly evaluate the offline target policy. This approach iteratively updates the world model by importance-sampling it according to the updated policy’s actions, allowing for adaptive alignment of the world model with the evolving policy. The results show significant improvement over state-of-the-art baselines in the D4RL environment, especially when only random or medium-expertise demonstrations are available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to learn from past experiences without needing more practice data. It uses a special kind of model called a diffusion world model that helps evaluate how well a policy would work in different situations. The method updates the model as it learns, making sure it stays accurate and helpful for evaluating the policy. This approach is particularly effective when there’s limited or mediocre-quality demonstration data available. |
Keywords
* Artificial intelligence * Alignment * Diffusion * Reinforcement learning
