Model-free depth-enhanced learning exploration method and device

A reinforcement learning, model-free technology applied in the field of machine learning to improve the efficiency of exploration

Active Publication Date: 2018-03-23
TSINGHUA UNIV
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  • Abstract
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Problems solved by technology

[0003] In view of this, this disclosure proposes a model-free deep reinforcement learning exploration method and device to solve the problem of how the deep reinforcement learning exploration method obtains training results that are more in line with actual application scenarios

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  • Model-free depth-enhanced learning exploration method and device
  • Model-free depth-enhanced learning exploration method and device

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Embodiment Construction

[0078] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numbers in the figures indicate functionally identical or similar elements. While various aspects of the embodiments are shown in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0079] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as superior or better than other embodiments.

[0080] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific implementation manners. It will be understood by those skilled in the art that the present disclosure may be practiced without some of the specific details. In some instances, methods, means, componen...

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Abstract

The invention relates to a model-free depth-enhanced learning exploration method and a device. The method comprises the following steps of acquiring a characteristic value according to a sample; inputting the characteristic value into a depth-enhanced learning model and processing the characteristic value to obtain an action value; inputting the characteristic value into a counting model to obtainan action counting value; and determining a decision action according to the action value and the action counting value. The actions of different execution times are selected, and the environment return value of each action can be obtained more comprehensively in the exploration process of the depth-enhanced learning. The exploration efficiency is improved.

Description

technical field [0001] The present disclosure relates to the technical field of machine learning, and in particular to a model-free deep reinforcement learning exploration method and device. Background technique [0002] Deep Reinforcement Learning, Deep Reinforcement Learning is a brand new algorithm that combines deep learning and reinforcement learning to realize end-to-end learning from Perception to Action. Simply put, it is the same as human beings, input perceptual information such as vision, and then directly output actions through a deep neural network, without hand-crafted artificial work in the middle. Deep reinforcement learning has the potential to enable robots to learn one or even multiple skills fully autonomously. Reinforcement learning is one approach to solving sequential decision-making problems. In recent years, deep reinforcement learning, using neural networks as the estimator of the algorithm, has achieved certain results in tasks based on image inp...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/084G06N3/045
Inventor 季向阳张子函张宏昌
Owner TSINGHUA UNIV
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