Joint learning method for features and strategies based on state features and subsequent features

A technology of state characteristics and learning methods, applied in the field of deep reinforcement learning, can solve problems such as low sample utilization, affect network training and convergence, increase network training costs, etc., to improve sample utilization efficiency, efficient policy learning, and learning speed accelerated effect

Active Publication Date: 2018-11-27
UNIV OF SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

On the one hand, low-quality samples will affect the training and convergence of the network. On the other hand, the network tra

Method used

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  • Joint learning method for features and strategies based on state features and subsequent features
  • Joint learning method for features and strategies based on state features and subsequent features
  • Joint learning method for features and strategies based on state features and subsequent features

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Embodiment Construction

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0016] The embodiment of the present invention provides a joint learning method of features and strategies based on state features and subsequent features, aiming to solve the problem of low utilization efficiency of feature learning samples in traditional deep reinforcement learning.

[0017] This program analyzes the reinforcement learning formula and designs a policy learning program in combination with a deep network. For the reinforcement lear...

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Abstract

The invention discloses a joint learning method for features and strategies based on state features and subsequent features. The method comprises: obtaining state features representing an input stateby learning mapping from the input state to the instant award; by learning mapping from state features to a value evaluation function, obtaining subsequent features; the obtained state features and the subsequent features being on different time resolution ratios, after the state features and subsequent features are fused, learning fusion results by using a variety of strategic learning networks.Compared with a traditional Agent network, the method uses sample information more efficiently. Compared with other algorithms, learning speed is obviously accelerated, and a network can converge faster and obtain better learning effect.

Description

technical field [0001] The invention relates to the field of deep reinforcement learning, in particular to a joint learning method of features and strategies based on state features and subsequent features. Background technique [0002] Deep Reinforcement Learning (Deep Reinforcement Learning) is a sequential decision-making learning method based on deep networks. It integrates deep learning and reinforcement learning, realizes end-to-end learning from state to action, and continuously interacts with the environment. Implement policy enhancements in . In high-dimensional complex problems, effective features are automatically extracted from perceptual information based on deep neural networks, and based on this, policy learning is performed and actions are directly output, that is, there is no hard-coded process in policy learning. Deep reinforcement learning can effectively solve the perceptual decision-making problems of agents (Agents) under high-dimensional complex probl...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N3/063
CPCG06N3/063G06N3/08
Inventor 查正军李厚强冯晓云李斌王子磊
Owner UNIV OF SCI & TECH OF CHINA
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