Summary of On Feasible Rewards in Multi-agent Inverse Reinforcement Learning, by Till Freihaut et al.
On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning
by Till Freihaut, Giorgia Ramponi
First submitted to arxiv on: 22 Nov 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 a crucial problem in Inverse Reinforcement Learning (IRL) for multi-agent systems. Traditional IRL methods struggle when rewards are inferred from equilibrium observations, as single Nash equilibria can be misleading. The authors propose entropy-regularized games to address this issue, ensuring unique equilibria and improved interpretability. They also investigate the impact of estimation errors and derive sample complexity results for multi-agent IRL across various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Inverse Reinforcement Learning (IRL) is a way to understand how computers or robots make decisions by analyzing what they do well. When many agents work together, it gets harder to figure out why they make certain choices. The problem is that we might get wrong ideas about how the agents work just by looking at one good outcome. This paper helps solve this issue by introducing a new way of playing games that makes sure there’s only one possible good outcome. They also study what happens when our guesses are not perfect and provide guidance on how to learn from multiple scenarios. |
Keywords
* Artificial intelligence * Reinforcement learning
