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FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game
Based on it, we present a novel multi-agent reinforcement learning framework, Factorized Multi-Agent MiniMax Q-Learning (FM3Q), which can factorize the joint minimax Q function into individual ones and iteratively solve for the IGMM-satisfied minimax Q functions for 2t0sMGs.
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning
To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation.
Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint
Then the design of the event-triggered strategy is formulated as a constrained Markov decision problem, and reinforcement learning finds the best communication protocol that satisfies the limited bandwidth constraint.
Multiagent Systems
