Summary of In-trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminates, by Shicheng Liu et al.
In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminates
by Shicheng Liu, Minghui Zhu
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 addresses the limitation of current inverse reinforcement learning (IRL) methods, which cannot learn incrementally from ongoing trajectories. The authors propose an online bi-level optimization framework that updates a learned reward function and corresponding policy as new state-action pairs are observed. They introduce a novel algorithm that achieves sub-linear local regret O(sqrt(T)+log T+sqrt(T)*log T) for general rewards and O(log T) for linear rewards. Experimental results validate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about learning from expert behavior in a way that can be updated as new information becomes available. It’s like trying to figure out what makes someone an expert at something, but instead of just seeing their final product, you get to see them work through different steps and decisions. The researchers came up with a new way to do this that works better than current methods. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
