| Abstract |
Reinforcement learning ( RL) is a widely utilized machine learning paradigm for addressing sequential decision-making problems. Its core principle involves enabling agents to learn optimal policies iteratively through feedback derived from interactions between an agent and the environment. As the demands for computational power and data scale of practical applications continue to escalate, the transition from single-agent intelligence to collective intelligence becomes an inevitable trend in the future development of artificial intelligence. Therefore, challenges and opportunities are abundant for RL. In this paper, grounded on the concept of deep multi-agent reinforcement learning( MARL) , the current theoretical dilemmas are refined and analyzed, including limited scalability, credit assignment, exploration-exploitation dilemma, non-stationarity and partial observability of information. Various solutions and their advantages and disadvantages proposed by researchers are elaborated. Typical training and learning environment of MARL and its practical applications in complex decision-making fields, such as smart city construction, gaming, robotics control and autonomous driving, are introduced. The challenges and future development direction of collaborative multi-agent reinforcement learning are summarized. © 2024 Science Press. All rights reserved. |
