Summary of A Bayesian Solution to the Imitation Gap, by Risto Vuorio et al.
A Bayesian Solution To The Imitation Gap
by Risto Vuorio, Mattie Fellows, Cong Lu, Clémence Grislain, Shimon Whiteson
First submitted to arxiv on: 29 Jun 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 A novel framework for imitation learning is proposed in this paper, addressing the “imitation gap” that can occur when an agent lacks information about the environment’s observability. The authors introduce Bayesian Imitation Gap (BIG), a solution that combines expert demonstrations with a prior specifying the cost of exploratory behavior. BIG uses Bayesian inverse reinforcement learning to infer a reward posterior and then learns a Bayes-optimal policy. The approach is shown to enable agents to explore optimally in environments where no reward signal can be specified, while still leveraging expert demonstrations when possible. The authors’ experiments demonstrate the effectiveness of BIG in handling imitation gaps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers created a new way for machines to learn from experts. They wanted to solve a problem called the “imitation gap,” which happens when an agent doesn’t have all the information it needs to make good decisions. The authors propose a solution called Bayesian Imitation Gap (BIG), which uses expert demonstrations and a special kind of math to figure out what rewards are important. This approach helps agents learn how to behave in situations where they don’t have all the information, but still follow the rules set by experts. |
Keywords
» Artificial intelligence » Reinforcement learning
