2 papers
cs.GT2024
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…
cs.GT2024
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…