Summary of Simple Ingredients For Offline Reinforcement Learning, by Edoardo Cetin et al.
Simple Ingredients for Offline Reinforcement Learning
by Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati
First submitted to arxiv on: 19 Mar 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 explores offline reinforcement learning on a novel testbed called MOOD, where trajectories come from heterogeneous sources. Existing methods struggle with diverse data when adding it to an offline buffer, showing a significant performance deterioration. The authors investigate several hypotheses explaining this failure and surprisingly find that scale is the key factor influencing performance. They show that simple methods like AWAC and IQL with increased network size overcome previous failures and outperform prior state-of-the-art algorithms on the D4RL benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning works well when datasets are highly connected to a target task, but what happens when you mix in data from different tasks? The authors of this paper found that existing methods don’t do well with diverse data. They tested several ideas to figure out why and discovered that it’s actually the amount of data that matters more than how complex the method is. By using simpler models with bigger networks, they were able to overcome these challenges and perform better than previous best efforts. |
Keywords
* Artificial intelligence * Reinforcement learning
