Summary of To Bootstrap or to Rollout? An Optimal and Adaptive Interpolation, by Wenlong Mou and Jian Qian
To bootstrap or to rollout? An optimal and adaptive interpolation
by Wenlong Mou, Jian Qian
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); Machine Learning (stat.ML)
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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 introduces a new class of Bellman operators called subgraph Bellman operators, which combine the strengths of bootstrapping and rollout methods in reinforcement learning. The authors derive an estimator that combines temporal difference (TD) and Monte Carlo (MC) methods, achieving optimal variance with finite-sample adaptivity. The error upper bound approaches the optimal variance achieved by TD, while also exhibiting sample complexity dependent on the occupancy measure of a selected subset of the state space. This framework is shown to be optimal and adaptive for policy evaluation, reconciling TD and MC methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make better decisions when playing games or making choices in uncertain situations. It creates new ways to estimate the value of taking different actions, combining two existing methods that have strengths and weaknesses. The new approach is shown to be both efficient (like one method) and adaptable (like the other), which makes it useful for many real-world applications. |
Keywords
» Artificial intelligence » Bootstrapping » Reinforcement learning
