Summary of Rethinking Optimal Transport in Offline Reinforcement Learning, by Arip Asadulaev et al.
Rethinking Optimal Transport in Offline Reinforcement Learning
by Arip Asadulaev, Rostislav Korst, Alexander Korotin, Vage Egiazarian, Andrey Filchenkov, Evgeny Burnaev
First submitted to arxiv on: 17 Oct 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 This novel algorithm, based on optimal transport, tackles offline reinforcement learning when data is provided by various experts with varying levels of optimality. The goal is to create an efficient policy by stitching together the best behaviors from the dataset. By rethinking offline reinforcement learning as an optimal transportation problem, this approach aims to find a policy that maps states to partial distributions of expert actions for each given state. Experimental results demonstrate improvements over existing methods on continuous control problems from the D4RL suite. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning uses data from various experts to create efficient policies. But what if some experts aren’t so good? To fix this, scientists created a new algorithm that thinks about offline reinforcement learning as an “optimal transportation problem”. This means finding a policy that takes states and matches them with the best expert actions for each state. The result is an improvement over other methods on control problems from D4RL. |
Keywords
* Artificial intelligence * Reinforcement learning
