Summary of Hybrid Inverse Reinforcement Learning, by Juntao Ren et al.
Hybrid Inverse Reinforcement Learning
by Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury
First submitted to arxiv on: 13 Feb 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 approach to imitation learning using a hybrid reinforcement learning (RL) framework, which combines online data with expert demonstrations. This allows for more robust learning from fewer expert examples while reducing computational waste by focusing the learner’s exploration on good states. The approach is based on a reduction from inverse RL to expert-competitive RL, enabling efficient policy search without requiring arbitrary state resets. The paper presents both model-free and model-based hybrid inverse RL algorithms with strong performance guarantees and empirically shows significant sample efficiency improvements over standard inverse RL and other baselines on continuous control tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for machines to learn from humans by combining different types of information. Instead of using only expert examples, the approach also uses online data collected as the machine learns. This helps reduce waste and makes learning more efficient. The idea is simple but effective: focus the machine’s exploration on good states and actions by showing it what an expert would do in similar situations. The results show that this approach works well in practice, using less data than other methods to achieve the same level of performance. |
Keywords
* Artificial intelligence * Reinforcement learning
