Hierarchical reinforcement learning training method and device based on imitation learning

A technology of reinforcement learning and training methods, applied in the field of machine learning, it can solve the problems of teaching data application, ignoring internal connections, etc., to reduce the search space and improve efficiency.

Pending Publication Date: 2020-05-12
INFORMATION SCI RES INST OF CETC
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] However, in the past, only the teaching data of imitation learning was used to pre-train the parameters of the hierarchical reinforcement learning model. The imitation learning and hierarchical reinforcement learning were divided into two different steps, and the teaching data was not applied to the hierarchical reinforcement learning step. Intrinsic connections in the training process are ignored

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  • Hierarchical reinforcement learning training method and device based on imitation learning
  • Hierarchical reinforcement learning training method and device based on imitation learning
  • Hierarchical reinforcement learning training method and device based on imitation learning

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Embodiment Construction

[0035] Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described here.

[0036] Application overview

[0037] figure 1 Shows the general process of reinforcement learning. Generally, a reinforcement learning system includes an agent and an execution environment. The agent continuously learns and optimizes its strategy through interaction and feedback with the execution environment. Specifically, the agent observes and obtains the state of the execution environment, and determines the actions to be taken according to the current state of the execution environment according to a certain strategy. Such actions act on the executi...

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Abstract

The invention discloses a hierarchical reinforcement learning training method and device based on imitation learning, and an electronic device. The method comprises the steps of obtaining teaching data of a human expert; conducting pre-training based on imitation learning by using the teaching data, and determining an initial strategy; and based on the initial strategy, performing retraining basedon reinforcement learning, and determining a training model. The teaching data is used for pre-training and re-training, priori knowledge and strategies are effectively utilized, the search space isreduced, and the training efficiency is improved.

Description

technical field [0001] The present application relates to the field of machine learning, and more specifically, relates to a hierarchical reinforcement learning training method, device and electronic equipment based on imitation learning. Background technique [0002] Reinforcement learning is one of the most concerned research directions in the field of artificial intelligence in recent years, and has achieved many dazzling results in many fields. An agent learns to behave in an environment by performing certain actions and observing the rewards or outcomes obtained from those actions. However, an important shortcoming of reinforcement learning is the curse of dimensionality. When the dimension of the system state increases, the number of parameters to be trained will increase exponentially, which will consume a large amount of computing and storage resources. [0003] Based on the inherent hierarchical and compositional nature of real-world tasks, hierarchical reinforceme...

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Application Information

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IPC IPC(8): G06N20/00G06N3/10
CPCG06N3/10G06N20/00
Inventor 唐思琦李明强陈思高放黄彬城
Owner INFORMATION SCI RES INST OF CETC
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