Collaborative battle method and device of intelligent agent

Abstract

The invention relates to a collaborative battle method and device of an intelligent agent, terminal equipment and a storage medium. The method comprises the following steps: determining a virtual air battle scene in which the intelligent agent carries out battle; according to the virtual air battle scene, determining action space information and state space information of one or more agents, and according to the state value, determining a reward value of an action corresponding to the state value; according to the virtual air battle scene, the action space information, the state space information and the reward value, training the initial reinforcement learning model, and when the initial reinforcement learning model is in a convergence state, obtaining a target reinforcement learning model; and using the target reinforcement learning model and the rule agent for fighting, and solving the problem that target reward is difficult and unstable. And when the strategy of the multiple agents is changed, reinforcement learning of the multiple agents cannot be affected.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method, device, terminal equipment and storage medium for cooperative combat of intelligent agents. Background technique [0002] When multiple unmanned combat aircraft make autonomous maneuver decisions in air combat, they need to carry out decision-making cognition and coordination. Because the environment of unmanned combat aircraft is relatively complex, and the coordination between aircraft needs to be considered, how to realize the autonomous control of combat aircraft is a research focus. [0003] Traditional UAV control relies on expert knowledge to deal with different situations through expert judgment on the environment and experience construction rules, but this requires experts to have high experience and knowledge, and it takes a lot of time and energy to consider all situations. With the development of artificial intelligence technology, d...

AI Technical Summary

Problems solved by technology

However, in multi-agent reinforcement learning, the environment is complex and changeable, the state space will grow exponentially with the increase of agents, the problem of dimension explosion may occur, and the calculation is complicated; at the same time, there are difficulties and instability in determining the target reward. The definition of the reward function will be affected by the different cooperation and tasks between multi-agents, and when the strategy of each agent changes, the strategies of other agents will also change, affecting the final convergence of the algorithm

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