Summary of A Model-based Approach For Improving Reinforcement Learning Efficiency Leveraging Expert Observations, by Erhan Can Ozcan et al.
A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations
by Erhan Can Ozcan, Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis
First submitted to arxiv on: 29 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 This paper tackles how to integrate expert opinions (without explicit information on expert actions) into deep reinforcement learning settings to enhance sample efficiency. By combining maximum entropy reinforcement learning objectives with behavioral cloning losses using forward dynamics models, the authors formulate an augmented policy loss function. They then propose an algorithm that automatically adjusts the weights of each component in this loss function. Experimental results on various continuous control tasks show that their approach outperforms benchmarks by effectively utilizing available expert observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making robots smarter! It’s trying to figure out how to use expert advice, like what a human would do in a situation, without actually knowing what the human did. The scientists come up with a new way of combining two different approaches to make better decisions faster. They test it on some robot control problems and show that it works really well. |
Keywords
* Artificial intelligence * Loss function * Reinforcement learning
