Summary of Sample Complexity Reduction Via Policy Difference Estimation in Tabular Reinforcement Learning, by Adhyyan Narang et al.
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
by Adhyyan Narang, Andrew Wagenmaker, Lillian Ratliff, Kevin Jamieson
First submitted to arxiv on: 11 Jun 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 studies the non-asymptotic sample complexity for pure exploration in contextual bandits and tabular reinforcement learning. Existing work in bandits shows that it’s possible to identify the best policy by estimating the difference between individual policies’ behaviors, which is cheaper than estimating each policy directly. However, RL complexities don’t take advantage of this and instead estimate each policy’s behavior directly. The paper answers this question positively for contextual bandits but negatively for tabular RL, showing a separation between the two. It also shows that almost sufficing to estimate only differences in RL: estimating a single reference policy and its deviations from other policies is sufficient. An algorithm instantiates this principle, achieving the tightest known bound on tabular RL sample complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to find the best way to make decisions when you don’t know what will happen next. It’s like trying to figure out which path to take in a forest without knowing where they lead. The research shows that in some cases, we can just look at how different paths behave and compare them to find the best one. But in other cases, this doesn’t work as well and we need to look at each path individually. The paper also introduces an algorithm that uses this idea to make decisions more efficiently. |
Keywords
» Artificial intelligence » Reinforcement learning
