Summary of Continuous Mean-zero Disagreement-regularized Imitation Learning (cmz-dril), by Noah Ford et al.
Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)
by Noah Ford, Ryan W. Gardner, Austin Juhl, Nathan Larson
First submitted to arxiv on: 2 Mar 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 proposes a novel imitation learning method called Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL), which enables high-performing agents in complex environments with minimal data and no known reward function. CMZ-DRIL uses reinforcement learning to minimize uncertainty among an ensemble of agents trained on expert demonstrations, creating a continuous mean-zero reward function from action disagreement. The method is tested in a waypoint-navigation environment and two MuJoCo environments, outperforming previous approaches in several key metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how computers can learn to do things by watching others. It introduces a new way for machines to learn called CMZ-DRIL. This approach lets computers create good actions even with only a few examples of what to do. The method doesn’t need specific rewards or lots of data. Instead, it uses the differences between computer versions trying to imitate an expert to guide its learning. Results show that CMZ-DRIL creates better actions than previous methods in various scenarios. |
Keywords
* Artificial intelligence * Reinforcement learning
