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Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning
Batuhan Yardim, Niao He
Mean-field games (MFG) have become significant tools for solving large-scale multi-agent reinforcement learning problems under symmetry. However, the assumption of exact symmetry l…
Learning to Steer Markovian Agents under Model Uncertainty
Jiawei Huang, Vinzenz Thoma, Zebang Shen +2
Designing incentives for an adapting population is a ubiquitous problem in a wide array of economic applications and beyond. In this work, we study how to design additional rewards…
When is Mean-Field Reinforcement Learning Tractable and Relevant?
Batuhan Yardim, Artur Goldman, Niao He
Mean-field reinforcement learning has become a popular theoretical framework for efficiently approximating large-scale multi-agent reinforcement learning (MARL) problems exhibiting…
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
Jiawei Huang, Niao He, Andreas Krause
We study the sample complexity of reinforcement learning (RL) in Mean-Field Games (MFGs) with model-based function approximation that requires strategic exploration to find a Nash…