QSIM: Mitigating Overestimation in Multi-Agent Reinforcement Learning via Action Similarity Weighted Q-Learning

QSIM: Mitigating Overestimation in Multi-Agent Reinforcement Learning via Action Similarity Weighted Q-Learning explores Develop QSIM to enhance stability and performance in multi-agent reinforcement learning by mitigating Q-value overestimation using action similarity weighted Q-learning.. Commercial viability score: 6/10 in Reinforcement Learning.