Deep reinforcement learning method and device based on visual converter

Abstract

The invention belongs to the technical field of artificial intelligence, and provides a deep reinforcement learning method and device based on a visual converter, and the method comprises the steps: constructing a deep reinforcement learning network structure based on the visual converter, wherein the visual converter comprises a multi-layer perceptron and a conversion encoder, and the conversion encoder comprises a multi-head attention layer and a feedforward network; initializing the weight of the deep reinforcement learning network, and constructing an experience playback pool according to the memory capacity; generating empirical data and putting the empirical data into an empirical playback pool through interaction between a greedy strategy and an operating environment; when the number of samples in the experience playback pool meets a preset value, randomly extracting a batch of training sample images from the training sample images, preprocessing the training sample images, and inputting the preprocessed training sample images into a deep reinforcement learning network for training; and when the deep reinforcement learning network satisfies a convergence condition, obtaining a reinforcement learning model. According to the method and device, the blank of application of the visual converter in the reinforcement learning field can be filled, the interpretability of the reinforcement learning method is improved, and learning training is more effectively carried out.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a deep reinforcement learning method and device based on a visual converter. Background technique [0002] In recent years, reinforcement learning has gradually become a research hotspot in the field of machine learning. An agent learns a strategy to maximize rewards or achieve a certain goal by interacting with the environment. Through the combination with deep learning methods, deep reinforcement learning methods have made breakthroughs in many artificial intelligence tasks, such as game games, robot control, group decision-making, automatic driving, etc. [0003] At present, deep reinforcement learning methods mainly include methods based on value functions, methods based on policy gradients, and methods based on the Actor-Critic framework. In the existing reinforcement learning network framework, the network structures adopted are mainly convolut...

AI Technical Summary

Problems solved by technology

However, transformers have not yet had a corresponding research in the field of reinforcement learning

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