Multi-Robot Reinforcement Learning Based on LCS and LS-SVM
杰 邵 · Artificial Intelligence and Robotics Research · 2013
本文提出了一种LCS和LS-SVM相结合的多机器人强化学习方法，LS-SVM获得的最优学习策略作为LCS的初始规则集。LCS通过与环境的交互，能更快发现指导多机器人强化学习的规则，为强化学习系统的动作选择提供实时、动态的反馈，使多机器人自主地学习到相互协作的最优策略。算法的分析和仿真表明多机器人学习空间大、学习速度收敛慢、学习效果不确定等问题得到很大的改善。 This paper presents a multi-robot reinforcement learning method combination LCS and LS-SVM, the optimal learning strategy LS-SVM obtained as an initial rule set of LCS. LCS interact with the environment, which can quickly find the guiding rules for multi-robot reinforcement learning, provide real-time, dynamic feedback, so that multi-robot autonomously learn the optimal strategy of mutual cooperation. Algorithm analysis and simulation show that a large space for multi-robot learning, the learning speed converges slowly, uncertainties and other learning problems can get a great improvement.