Summary of Is Value Learning Really the Main Bottleneck in Offline Rl?, by Seohong Park et al.
Is Value Learning Really the Main Bottleneck in Offline RL?
by Seohong Park, Kevin Frans, Sergey Levine, Aviral Kumar
First submitted to arxiv on: 13 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 This paper investigates the bottlenecks hindering offline reinforcement learning (RL) from achieving similar performance as imitation learning. Despite the potential for using a value function with lower-quality data, current results indicate that offline RL often performs worse than imitation learning. The study aims to understand the main limitations in current offline RL algorithms by analyzing three key components: value learning, policy extraction, and policy generalization. Surprisingly, the choice of policy extraction algorithm is found to significantly impact performance and scalability, while imperfect policy generalization on test-time states outside the training data support is identified as a major barrier to improving performance. To address this issue, two simple test-time policy improvement methods are proposed and shown to lead to better results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning (RL) has the potential to perform similarly or even better than imitation learning with lower-quality data by using a value function. However, current research shows that offline RL often performs worse than imitation learning, making it unclear what holds back its performance. The paper investigates why this is the case and finds that the choice of policy extraction algorithm significantly impacts performance and scalability. It also identifies imperfect policy generalization on test-time states outside the training data support as a major barrier to improving performance. To address these issues, the study proposes two simple test-time policy improvement methods that lead to better results. |
Keywords
* Artificial intelligence * Generalization * Reinforcement learning
