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Characteristic of temperature-based reinforcement learning in learning-parameters - characteristic of convergence of learning and construction of state-space -

机译:基于温度的增强学习参数的特征 - 学习趋同特征及状态空间施工 -

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摘要

During STL (S-Temperature based reinforcement Learning), the robot system can compose the coordination of state-action. After completing the learning, the robotic system transits seamlessly to the task-achieving process. When the robot system accidentally confronts with unknown state of sensory data, it restarts the learning. This paper clarifies the learning characteristic of convergence of learning and construction of state-space in learning parameters by computer simulation experiment.
机译:在STL(基于S温度的强化学习)期间,机器人系统可以构成状态动作的协调。完成学习后,机器人系统无缝运输到任务实现过程。当机器人系统意外地面对有未知的感官数据状态时,它重新启动学习。本文通过计算机仿真实验阐明了学习参数学习参数学习与建设的学习特征。

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