Summary of Accelerating Inverse Reinforcement Learning with Expert Bootstrapping, by David Wu and Sanjiban Choudhury
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping
by David Wu, Sanjiban Choudhury
First submitted to arxiv on: 4 Feb 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 presents a novel approach to inverse reinforcement learning (IRL), which is a type of machine learning that learns the reward function from expert demonstrations. Traditional IRL methods search for the optimal reward function by solving a reinforcement learning problem in an inner loop, but this can be inefficient and slow. The authors propose two simple yet effective recipes to improve the efficiency of IRL: using expert transitions in the replay buffer and incorporating expert actions into Q-value bootstrapping. These techniques accelerate learning by providing more informative and accurate feedback to the learner. The paper demonstrates significant gains over a baseline method on various benchmark tasks, including MuJoCo, with speedups ranging from 2.13x to 3.36x. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to learn how something works by watching someone else do it. Right now, computers have trouble doing this because they need to figure out what makes the other person happy or sad. The authors found a way to make this process faster and better by using the expert’s actions as a guide. They tested their method on various tasks and showed that it can be up to 3 times faster than current methods. |
Keywords
* Artificial intelligence * Bootstrapping * Machine learning * Reinforcement learning
