Reinforcement learning strategy learning method based on potential action representation space

A strategy learning and representation space technology, applied in the field of machine learning, can solve problems such as costing a lot of time and money, mechanical damage, damage to intelligent systems, etc., to achieve the effect of improving generalization ability, stability, and learning speed

Pending Publication Date: 2020-11-17
TIANJIN UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

However, for complex intelligent systems in practical applications, it takes a lot of time and money to collect sufficient learning samples, and there is even a risk of damaging the intelligent system. For example, collecting learning samples of robots performing tasks in dangerous environments may cause mechanical injury
It can be seen that the sample utilization rate and learning efficiency are an important bottleneck in the practical application of deep reinforcement learning.

Method used

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  • Reinforcement learning strategy learning method based on potential action representation space
  • Reinforcement learning strategy learning method based on potential action representation space
  • Reinforcement learning strategy learning method based on potential action representation space

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Embodiment Construction

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to 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.

[0021] The embodiment of the present invention discloses a reinforcement learning strategy learning method based on latent action representation space, which specifically includes: model modeling, optimization target construction, and optimization problem solving. In the implementation of the present invention, the interaction process between the agent and the environment is modeled as a Markov decision process (MDP), which can be represented by a...

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Abstract

Sample utilization rate and learning efficiency are an important bottleneck problem of deep reinforcement learning in practical application. For the real world, in order to quickly and accurately obtain a universal strategy, the invention provides a reinforcement learning strategy learning method based on a potential action representation space, and the method learns the strategy in the potentialspace of the action and maps the action representation to the real action space; a strategy in the method is mapping from a state to action representation, the search space of strategy learning can bereduced, and the strategy learning efficiency is improved; according to the method, mature supervised learning can be selected for offline learning for representation of actions, the learning speed can be further increased, and the stability is improved. Besides, as long as the characteristics of the adopted actions are similar, even for tasks different from the training strategy, the learned strategy can be generalized into the action space of the current execution task under the fine tuning of a small number of learning samples, and the generalization ability of strategy expression is greatly improved.

Description

technical field [0001] The present invention relates to the field of machine learning, and more specifically relates to a reinforcement learning strategy learning method based on latent action representation space. Background technique [0002] Deep reinforcement learning achieves direct control from input to output through end-to-end learning, enabling reinforcement learning to be extended to decision-making problems with high-dimensional state and action spaces that were previously difficult to deal with. Technical support has become a research hotspot that has attracted much attention. So far, deep reinforcement learning has been successfully applied to many fields: such as robotics, games, parameter optimization, video prediction, machine translation, automatic driving, intelligent transportation systems, multi-agent systems, aerospace systems and digital art intelligent systems, etc. [0003] An important prerequisite for the success of deep reinforcement learning is a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 赵婷婷王雨芯陈亚瑞杨巨成王嫄任德华
Owner TIANJIN UNIV OF SCI & TECH
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