Summary of Mean-field Sampling For Cooperative Multi-agent Reinforcement Learning, by Emile Anand et al.
Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning
by Emile Anand, Ishani Karmarkar, Guannan Qu
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Systems and Control (eess.SY); Optimization and Control (math.OC)
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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 algorithm, SUBSAMPLE-MFQ, addresses the challenges of multi-agent reinforcement learning (MARL) by efficiently balancing sequential global decision-making with local agent interactions. This algorithm learns a policy for systems with n agents in time polynomial in k, where k is the number of subsampled agents. The learned policy converges to the optimal policy on the order of O(1/√k) as k increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SUBSAMPLE-MFQ algorithm and decentralized randomized policy are designed for systems with multiple agents. By efficiently learning a policy, this method can improve decision-making in complex systems. This is achieved by using subsampled agents to reduce the size of the joint state and action spaces. |
Keywords
» Artificial intelligence » Reinforcement learning
