多智能体深度强化学习的若干关键科学问题

Reinforcement learning has been used to solve sequence decision problems without models for decades. However, it often faces great challenges in dealing with high-dimensional problems. In recent years, with the rapid development of deep learning, it promotes that reinforcement learning can provide the optimized strategy for complex and high-dimensional multi-agent systems to efficiently perform the target tasks in challenging environments. This paper reviews on the principles of reinforcement learning and deep reinforcement learning, puts forward the closed-loop control framework of learning systems, and investigates the existing important problems and corresponding methods for the deep reinforcement learning of multi-agent systems, including multi-agent reinforcement learning algorithmic framework, non-static environment, partially observability, and so on. The merits and drawbacks of these investigated methods are analyzed, and some related applications are summarized. This paper also provides some new insights into various research directions of multi-agent reinforcement learning, and related ideas for better application development in the future.