Summary of Offline-to-online Multi-agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration, by Hai Zhong et al.
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration
by Hai Zhong, Xun Wang, Zhuoran Li, Longbo Huang
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a novel framework called Offline Value Function Memory with Sequential Exploration (OVMSE), designed to address the challenges of offline-to-online multi-agent reinforcement learning (MARL). The proposed OVMSE framework consists of two components: Offline Value Function Memory (OVM) and decentralized Sequential Exploration (SE). The OVM mechanism computes target Q-values, preserving knowledge gained during offline training and ensuring smoother transitions. The SE strategy utilizes the pre-trained offline policy for exploration, significantly reducing the joint state-action space to be explored. Experimental results on the StarCraft Multi-Agent Challenge (SMAC) demonstrate that OVMSE outperforms existing baselines in terms of sample efficiency and overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline-to-online reinforcement learning has become a powerful tool, using offline data for initialization and online fine-tuning to improve both sample efficiency and performance. However, most research has focused on single-agent settings, leaving the multi-agent extension largely unexplored. The O2O MARL framework aims to tackle two critical challenges: preserving knowledge gained during offline training and efficiently exploring the large joint state-action space. The proposed solution combines an Offline Value Function Memory (OVM) mechanism with a decentralized Sequential Exploration (SE) strategy. This approach enables smoother transitions from offline-to-online phases, ensuring better sample efficiency and overall performance. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning
