2020
ICML
ICML 2020
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
Abstract
Multi-agent reinforcement learning (MARL) achieves significant empirical successes. However, MARL suffers from the curse of many agents. In this paper, we exploit the symmetry of agents in MARL. In the most generic form, we study a mean-field MARL problem. Such a mean-field MARL is defined on mean-field states, which are distributions that are supported on continuous space. Based on the mean embedding of the distributions, we propose MF-FQI algorithm, which solves the mean-field MARL and establishes a non-asymptotic analysis for MF-FQI algorithm. We highlight that MF-FQI algorithm enjoys a “blessing of many agents” property in the sense that a larger number of observed agents improves the performance of MF-FQI algorithm.
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Interdisciplinary Bridge
— Machine Learning and Reinforcement Learning
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Keyword Pioneer
— curse of many agent
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy
Authors
Topics
Machine Learning > Optimization & Theory > Statistical Learning
Reinforcement Learning > Methods > Multi-Agent Systems
Reinforcement Learning > Applications > Value Iteration
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Learning Types > Multi-Agent Systems
Reinforcement Learning > Applications > Multi-Agent Systems