Knowledge Driven Course of Action Planning for Intelligent Game Confrontation
Aiming at the problems of conflict between exploration and utilization,sparse reward signals,low data utilization rate,and difficulty in stable convergence in solving the practical course of action planning for Intelligent Game Confrontation based on deep reinforcement learning.The knowledge-based type-learning intelligent generation mode is analyzed,and the knowledge driven method is proposed.The course of planning model of intelligent game confrontation from the aspects of rule-based teaching,data-based learning and problem-based guidance and other aspects is constructed,which provides theoretical support for improving the exploration utilization efficiency,accurate reward function and accelerating algorithm convergence.The difficult problems of solving the intelligent game confrontation problem based on reinforcement learning are discussed,and the more practical development direction of the next step deep enforcement learning algorithm is pointed out.
deep reinforcement learningintelligent game confrontationknowledge drivencourse of action planning
万方数据
维普
