Summary of An Introduction to Centralized Training For Decentralized Execution in Cooperative Multi-agent Reinforcement Learning, by Christopher Amato
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning
by Christopher Amato
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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 delves into the realm of Multi-agent reinforcement learning (MARL), a burgeoning field that has garnered significant attention in recent years. The authors explore three primary approaches to MARL: centralized training and execution (CTE), centralized training for decentralized execution (CTDE), and Decentralized training and execution (DTE). They aim to shed light on the strengths, weaknesses, and potential applications of each methodology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MARL is a way for machines to learn together, making decisions that benefit everyone. The paper talks about three ways to do this: one where everything is controlled from the top, another where some things are controlled from the top but others aren’t, and finally, one where nothing is controlled from the top. This research can help us understand which approach works best in different situations. |
Keywords
* Artificial intelligence * Attention * Reinforcement learning
