In the past two years, machine intelligence has made a major breakthrough. Like the nine-segment master Li Shishi defeated Alpha Go, the robot developed by the DeepMind team surpassed the human level in Atari games. These breakthroughs are mainly due to the intensive training model from the perspective of visual, speech, semantic perception and motion feedback based on deep learning. This paper conceptually analyzes the main points of deep reinforcement learning, and some of them are summarized in the report of Deep Reinforcement Learning [1] in ICML 2016 Tutorial.
Reinforced learning, in which the robot automatically adjusts the strategy based on the punishment and incentives that the action takes in the environment. Through training, the robot learns a set of strategies: action A should be taken in the environmental state S, and the maximum cumulative reward V can be obtained.
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Reinforcement learning has a rich interdisciplinary background, including economics, engineering, game theory in neuroscience, optimal control, and conditioned reflex systems.
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