Summary of A Non-monolithic Policy Approach Of Offline-to-online Reinforcement Learning, by Jaeyoon Kim et al.
A Non-Monolithic Policy Approach of Offline-to-Online Reinforcement Learning
by JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain
First submitted to arxiv on: 31 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 The proposed offline-to-online reinforcement learning (RL) method leverages both pre-trained offline policies and online policies trained for downstream tasks, aiming to improve data efficiency and accelerate performance enhancement. The method harmonizes the advantages of the offline policy, referred to as exploitation, with those of the online policy, referred to as exploration, without modifying the offline policy. Our methodology demonstrates superior performance compared to Policy Expansion (PEX). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline-to-online reinforcement learning aims to improve data efficiency and accelerate performance enhancement by combining pre-trained offline policies and online policies trained for downstream tasks. The proposed method harmonizes the advantages of exploitation from the offline policy with exploration from the online policy without modifying the offline policy, leading to superior performance compared to Policy Expansion (PEX). |
Keywords
* Artificial intelligence * Reinforcement learning
