Summary of Refining Minimax Regret For Unsupervised Environment Design, by Michael Beukman et al.
Refining Minimax Regret for Unsupervised Environment Design
by Michael Beukman, Samuel Coward, Michael Matthews, Mattie Fellows, Minqi Jiang, Michael Dennis, Jakob Foerster
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Bayesian level-perfect minimax regret (BLP), a refinement of the minimax regret objective in unsupervised environment design. In reinforcement learning, agents are trained on environment configurations (levels) generated by an adversary maximizing regret. Minimax regret (MMR) policies have desirable robustness guarantees, but learning stagnates once the agent reaches the maximum regret bound. The proposed BLP refinement overcomes this limitation and ensures consistent Perfect Bayesian policy behavior across all levels. The ReMiDi algorithm is introduced to produce a BLP policy at convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers create a new way to train machines in unsupervised environments. They want the machines to learn how to adapt to different situations without being told what’s right or wrong. The problem they’re trying to solve is that when the machine reaches its best performance, it stops learning because there’s no motivation to improve further. To fix this, they develop a new method called Bayesian level-perfect minimax regret (BLP), which allows the machine to continue learning and improving. |
Keywords
* Artificial intelligence * Reinforcement learning * Unsupervised
