Robot control method based on offline model pre-training learning DDPG algorithm

Abstract

The invention provides a robot control method based on an off-line model pre-training learning DDPG algorithm, and the method comprises the following steps: collecting the training data of a 2D dummy in an off-line environment, and carrying out the preprocessing of the training data to obtain a training data set; constructing and initializing an artificial neural network and initializing parameters; pre-training the evaluation network and the action network offline by using the training data set; initializing a target network by using the pre-trained evaluation network, and storing state conversion data into a storage buffer by the intelligent agent to serve as an online data set for training an online network; training an online strategy network and an online Q network by using the online data set, and updating the online strategy network and the online Q network by using a DDQN structure; carrying out soft updating, and controlling the state of the 2D dummy. According to the method, the efficiency is higher, the generated Q value is more accurate, the average reward value is higher, the learning strategy is more stable and reliable, the convergence rate is increased, the obtained accumulated reward value reaches a higher level, and the robot can quickly arrive at the destination.

Description

technical field

[0001] The present invention relates to the technical field of robot control, in particular to a robot control method based on the DDPG algorithm of off-line model pre-training learning. Background technique

[0002] Reinforcement learning is an important branch of machine learning in which an agent learns to behave in an environment by performing certain actions and observing the rewards or outcomes obtained from those actions. It mainly contains four elements: agent, environment state, action and reward. The goal of reinforcement learning is that the agent performs actions in the positive direction as much as possible according to the positive feedback of the environment to learn a good strategy and obtain the most cumulative rewards.

[0003] At present, deep reinforcement learning has had an important impact on the simulation control, motion control, indoor and outdoor navigation, and simultaneous positioning of robots, enabling robots to automatically l...

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