多智能体强化学习及其在足球机器人角色分配中的应用

足球机器人系统是一个典型的多智能体系统, 每个机器人球员选择动作不仅与自身的状态有关, 还要受到其他球员的影响, 因此通过强化学习来实现足球机器人决策策略需要采用组合状态和组合动作. 本文研究了基于智能体动作预测的多智能体强化学习算法, 使用朴素贝叶斯分类器来预测其他智能体的动作. 并引入策略共享机制来交换多智能体所学习的策略, 以提高多智能体强化学习的速度. 最后, 研究了所提出的方法在足球机器人动态角色分配中的应用, 实现了多机器人的分工和协作.

Multi-agent reinforcement learning and its application to role assignment of robot soccer

Robot soccer is a typical multi-agent system. The action selected by each robot player not only depends on the current field state, but is also impacted by other players. Hence, the decision-making strategy of robot soccer obtained by reinforcement learning needs the information of the joint-state and the joint-action of multiple agents. A multi-agent reinforcement learning method based on the action prediction of agents is proposed. The Naive Bayes classifier is applied to predict the actions of other agents. Moreover, the sharing-policy mechanism is introduced into multi-agent reinforcement learning system for exchanging the learning policies among agents. It can increase the learning speed effectively. Finally, the proposed approach is applied to learn the role assignment strategy in realizing the cooperation and coordination between robots.