A Collaborative Method for Soccer Robots Based on Reinforcement Learning

A football robot and reinforcement learning technology, applied in the field of football robot collaboration based on reinforcement learning, can solve problems such as low collaboration efficiency, and achieve the effect of improving watchability and improving collaboration efficiency.

Active Publication Date: 2021-10-01
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem of low cooperation efficiency between soccer robots in the soccer robot competition in the above-mentioned prior art, the present invention proposes a soccer robot collaboration method based on reinforcement learning, which is based on adding communication-based Sarsa( The λ) algorithm constructs the basic model of reinforcement learning of soccer robots, and achieves high cooperation efficiency between each other through the reinforcement learning model and the communication between soccer robots. The specific technical scheme is as follows:

Method used

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  • A Collaborative Method for Soccer Robots Based on Reinforcement Learning
  • A Collaborative Method for Soccer Robots Based on Reinforcement Learning
  • A Collaborative Method for Soccer Robots Based on Reinforcement Learning

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Embodiment 1

[0023] refer to figure 1 , in an embodiment of the present invention, a football robot collaboration method based on reinforcement learning is provided, specifically including the method:

[0024] S1. Construct the basic reinforcement learning model of the soccer robot based on the Sarsa(λ) algorithm with communication, and set the reward and punishment mechanism r of the basic reinforcement learning model.

[0025] refer to figure 2 , it can be seen that the principle of the basic model of reinforcement learning is: the football robot chooses an action in the state of perceiving the current environment, and at this time the environment state migrates to a new state, and correspondingly, the new state generates a reinforcement signal to feed back to the football robot, football The robot determines the next action according to the current environment information and the reinforcement signal; wherein, the key of the reinforcement learning of the football robot in the present ...

Embodiment 2

[0090]The method in Embodiment 1 is verified by using the HFO experimental platform, specifically, it consists of m offensive players and n defensive players. Among them, defensive players include goalkeepers, and n≥m. Half-court offensive missions are played on one half of the football field and begin near the half-court line with the ball in the possession of the offensive players; see Figure 4 , the figure shows a classic 4v5 HFO platform, where the white solid circle is the ball, four offensive players, and five defensive players including the goalkeeper; To score a goal successfully, it is necessary to learn the three operations of the offensive player's passing, dribbling and shooting through the basic model of reinforcement learning, and simulate the actions of the defensive player trying to stop the offensive player.

[0091] Preferably, the present invention first carries out 30 groups of experiments to analyze errors between football robots having communication and...

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Abstract

The invention discloses a soccer robot cooperation method based on reinforcement learning, the method comprising: S1, constructing a reinforcement learning basic model of a soccer robot based on a Sarsa(λ) algorithm with communication added, and setting the reinforcement learning basic model The reward and punishment mechanism r; S2, based on the distance and angle between the soccer robots to define a specified number of state variables; S3, set the operable action set of the soccer robot, the soccer robot is based on the reward and punishment mechanism r and the state variables and the soccer robot Communicate with each other to select the next action; the present invention sets up a reward and punishment mechanism in the basic model of reinforcement learning established, so that the football robot can select the next action according to the current environment and the reward and punishment mechanism, and learn by communicating with each other. And updates, effectively improving the collaboration efficiency of soccer robots.

Description

technical field [0001] The invention belongs to the field of soccer robots, and in particular relates to a soccer robot collaboration method based on reinforcement learning. Background technique [0002] As a typical multi-soccer robot system, the football robot competition provides a good experimental platform for the study of intelligence theory and the integrated application of various technologies. The capability requirements are getting stronger and stronger, which involves a series of research topics such as robot positioning, path planning, coordinated control, target tracking and decision-making. [0003] In recent years, many scholars and experts have made a lot of research results. For example, the Chinese patent application number 201120008202.2 discloses an intelligent robot competition device, including mechanical parts and circuit control parts. The mechanical parts include ball tables, consoles and robots. , the circuit control part includes a control module ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B25J9/16
Inventor 胡丽娟梁志伟李汉辉
Owner NANJING UNIV OF POSTS & TELECOMM
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