Summary of Offline Imitation Learning with Model-based Reverse Augmentation, by Jie-jing Shao et al.
Offline Imitation Learning with Model-based Reverse Augmentation
by Jie-Jing Shao, Hao-Sen Shi, Lan-Zhe Guo, Yu-Feng Li
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel model-based framework for offline imitation learning, called SRA (Self-paced Reverse Augmentation). The challenge in offline IL is the covariate shift between expert observations and actual distributions encountered by the agent. Existing solutions introduce supplementary data or build forward dynamic models but are often over-conservative in out-of-expert-support regions. SRA builds a reverse dynamic model to generate trajectories leading to expert-observed states, then uses reinforcement learning to learn from augmented trajectories, exploring expert-unobserved states while maximizing long-term returns. This framework mitigates the covariate shift and achieves state-of-the-art performance on offline imitation learning benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to copy someone’s behavior without actually seeing them do it. That’s a big challenge! The experts may know what to do in some situations, but not others. The paper proposes a new way to learn from expert demonstrations and make decisions even when we’re not sure what the expert would do. This is important because it allows us to apply what we’ve learned to new situations. The method uses a combination of old and new ideas to help the agent explore new situations while still making good decisions. |
Keywords
» Artificial intelligence » Reinforcement learning
