Summary of Test-time Regret Minimization in Meta Reinforcement Learning, by Mirco Mutti et al.
Test-Time Regret Minimization in Meta Reinforcement Learning
by Mirco Mutti, Aviv Tamar
First submitted to arxiv on: 4 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 The researchers introduce a framework for meta-reinforcement learning that learns an optimal policy for any test task efficiently. The focus is on regret minimization against the optimal policy in an unknown test task, where the agent has undergone training on a finite set of tasks modeled through Markov decision processes with varying dynamics. The study shows that achieving O(M^2 (H)) regret is possible under a separation condition. Furthermore, it provides a novel lower bound for test-time regret minimization under separation, demonstrating that a linear dependence with M is unavoidable. Additionally, the researchers introduce a family of stronger assumptions, called strong identifiability, which enables algorithms to achieve fast rates and sublinear dependence with M simultaneously. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how an artificial intelligence agent can learn to perform well on any new task it encounters after being trained on several tasks. The goal is to find the best strategy for a given task without having to start from scratch each time. The researchers investigate when this process can be done efficiently and with minimal regret compared to the optimal policy. |
Keywords
» Artificial intelligence » Reinforcement learning
