Summary of Towards Generalized Inverse Reinforcement Learning, by Chaosheng Dong et al.
Towards Generalized Inverse Reinforcement Learning
by Chaosheng Dong, Yijia Wang
First submitted to arxiv on: 11 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 proposes a solution to the problem of learning the basic components of a Markov decision process (MDP) given observed behavior, known as generalized inverse reinforcement learning (GIRL). The authors address two key challenges in GIRL: quantifying the discrepancy between the observed policy and the underlying optimal policy, and mathematically characterizing the underlying optimal policy when the MDP’s basic components are unobservable or partially observable. To tackle these challenges, the paper proposes a mathematical formulation for GIRL and develops a fast heuristic algorithm. The proposed approach is evaluated on both finite and infinite state problems, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to figure out the building blocks of a Markov decision process (MDP) based on what we see people doing. This is hard because we don’t know exactly how the MDP works or even what it looks like. The researchers solve two big problems: first, they find a way to measure how different our observations are from the ideal behavior. Second, they develop a way to describe this ideal behavior when some parts of the MDP are hidden or unknown. To do this, they create a new formula and an efficient algorithm. They test their approach on both simple and complex problems and show that it works well. |
Keywords
* Artificial intelligence * Reinforcement learning
