Summary of Higher Replay Ratio Empowers Sample-efficient Multi-agent Reinforcement Learning, by Linjie Xu et al.
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
by Linjie Xu, Zichuan Liu, Alexander Dockhorn, Diego Perez-Liebana, Jinyu Wang, Lei Song, Jiang Bian
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 A Reinforcement Learning (RL) technique, Multi-Agent Reinforcement Learning (MARL), faces a significant challenge: poor sample efficiency. Compared to single-agent RL, MARL’s partial observability, non-stationary training, and vast strategy space make it more challenging. While new methods are being developed to enhance sample efficiency, this paper focuses on the episodic training mechanism used in many MARL algorithms. Each training step collects tens of frames, but only one gradient update is performed. The authors argue that this episodic training contributes to poor sample efficiency and propose increasing the frequency of gradient updates per environment interaction (Replay Ratio or Update-To-Data ratio). To demonstrate its effectiveness, three MARL methods are evaluated on six SMAC tasks. The results show that a higher replay ratio significantly improves sample efficiency for MARL algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MARL is a type of RL where multiple agents learn together. One problem with MARL is that it’s not very efficient at using the data it collects. This paper looks at why this might be happening and proposes a solution. Right now, when we train our MARL models, we collect lots of data but only use it to make one update to our model. The authors think this is a waste and propose increasing how often we update our model based on the data we’ve collected. They tested three different ways of doing this on six different tasks and found that it really helps improve the efficiency of MARL. |
Keywords
» Artificial intelligence » Reinforcement learning
