Model-free deep reinforcement learning exploration method and device

An enhanced learning, model-free technology, applied in the field of machine learning, to achieve the effect of improving the efficiency of exploration

Active Publication Date: 2020-04-21
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 deep reinforcement learning exploration method and device
  • Model-free deep reinforcement learning exploration method and device
  • Model-free deep reinforcement 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 present disclosure relates to a model-free deep reinforcement learning exploration method and device, the method comprising: obtaining feature values ​​according to samples; inputting the feature values ​​into a deep reinforcement learning model for processing to obtain action values; inputting the feature values ​​into counting The model obtains an action count value; and a decision-making action is determined according to the action value and the action count value. By selecting actions with different execution times, in the exploration process of deep reinforcement learning, the environmental reward value of each action can be obtained more comprehensively, thereby improving the exploration efficiency.

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...

Claims

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Application Information

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Patent Type & Authority Patents(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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