Temporal Memory Reinforcement Learning for the Autonomous Micro-mobile Robot Based-behavior

This paper presents temporal memory reinforcement learning for the autonomous micro-mobile robot based-behavior. Human being has a memory oblivion process, i.e. the earlier to memorize, the earlier to forget, only the repeated thing can be remembered firmly. Enlightening forms this, and the robot need not memorize all the past states, at the same time economizes the EMS memory space, which is not enough in the MPU of our AMRobot. The proposed algorithm is an extension of the Q-learning, which is an incremental reinforcement learning method. The results of simulation have shown that the algorithm is va|id.     

基于连接增强式学习的移动机器人控制

采用基于行为的控制方法，机器人在不知道外界精确模型的条件下，利用增强式学习自主完成给定的任务，机器人在学习过程中需要对行为状态进行记忆，连接增强式学习利用多层感知器逼近Q函数，泛化状态空间，节约了存储容量，仿真结果证明了这种算法的有效性，解决了基于查表增强式学习不适用连续状态空间的缺陷，为移动机器人进一步实用化提供了依据。     

基于替代传导径迹的多智能体增强式学习

提出一种多智能体增强式学习方法，每个智能体在学习过程中将其他智能体和环境区分开来，并且通过维持其他智能体的替代传导径迹来预测它们的行为，从而也确定了自身的行为。该算法不需要知道其他智能体的Q函数结构和奖赏函数结构，适用条件宽松。仿真结果证明了所提出学习算法的有效性，而且相对于集中式Q学习效率有很大的提高。