Summary of Oracle-efficient Reinforcement Learning For Max Value Ensembles, by Marcel Hussing et al.
Oracle-Efficient Reinforcement Learning for Max Value Ensembles
by Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 proposes a novel approach to reinforcement learning in large or infinite state spaces by improving upon heuristic base policies. The authors aim to compete with the max-following policy, which at each state follows the action of whichever constituent policy has the highest value. They develop an efficient algorithm that learns to compete with this policy, given only access to the constituent policies themselves. This is achieved without requiring knowledge of the optimal or max-following policy itself. The authors demonstrate the effectiveness and behavior of their algorithm on several robotic simulation testbeds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us solve a tricky problem in machine learning by improving how we use simple rules to make decisions. These rules are like “constituent policies” that can be combined in smart ways to make better choices. The authors show that their new method is efficient and works well on robotic simulations, which could lead to breakthroughs in fields like robotics and artificial intelligence. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
