Rethinking Exploration and Experience Exploitation in Value-Based Multi-Agent Reinforcement Learning
Cooperative Multi-Agent Reinforcement Learning (MARL) focuses on developing strategies to effectively train multiple agents to learn and adapt policies collaboratively.Despite being a relatively new area of research, most MARL methods are based on well-established approaches used in single-agent deep learning tasks due to their proven effectiveness