Multi-agent reinforcement learning for complex sequential decision-making - Qiu, Wei (Nanyang Technological University, 2024)
Many tasks involve multiple agents and require sequential decision-making policies to achieve common goals, such as football games, real-time strategy games, and traffic light control in the road network. To obtain the policies of all agents, these problems can be modeled as multi-agent systems and solved with multi-agent reinforcement learning (MARL). However, optimizing policies in multi-agent scenarios is non-trivial due to complex multi-agent behaviors and the non-stationary nature of the environments’ complex dynamics. Agents’ behaviors and interactions with other agents cause the environment’s states and agents’ observations to change over time, making it challenging to develop effective policies that perform well over time. In addition, partial observability, where agents have limited or incomplete information about the environment, also complicates the problem. Moreover, the inherent uncertainty in the environment’s dynamics makes decision-making unstable.