Heterogeneous multi-agent collaborative decision-making method based on depth deterministic policy gradient

Abstract

The invention relates to a heterogeneous multi-agent collaborative decision-making method based on a depth deterministic policy gradient, belonging to the collaborative decision-making field of a heterogeneous intelligent unmanned system, comprising the following steps of: firstly, defining heterogeneous multi-agent characteristic attributes and reward and punishment rules, defining multi-agent state space and action space, and constructing multi-agent motion environment for collaboratively making decision; then, establishing an actor module for decision-making action and a critic module for evaluating feedback based on the depth-deterministic strategy gradient algorithm, and training the parameters of the learning model; using the trained model to obtain the multi-agent state sequence; and evaluating the situation of the multi-agent motion state sequence according to the reward and punishment rules set in the environment. The invention may construct reasonable sports environment according to actual needs, achieve the purpose of intelligent sensing and strategy optimization through the synergy between multiple agents in the system, and has a positive effect on the development of the unmanned system field in China.

Description

technical field [0001] The invention belongs to the field of collaborative decision-making of heterogeneous intelligent unmanned systems, and in particular relates to a method for collaborative decision-making of heterogeneous multi-agents based on deep deterministic strategy gradients. Background technique [0002] In recent years, the rapid development of information technology and intelligent perception technology has laid an important foundation for advanced intelligent behaviors such as the perception of complex environments, precise intelligent decision-making, and multi-machine task coordination. The research on intelligent unmanned systems has now become a landmark achievement in the development of artificial intelligence. The complexity of its tasks and the uncertainty of the dynamic environment determine that the system must have strong adaptive and autonomous capabilities. [0003] Traditional intelligent ant colony (Swarm Intelligence) [1] Beginning in 1959, Fre...

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Problems solved by technology

Its advantage is that it has strong expressive ability and good decision-making ability. The disadvantage is that actions and states are discrete.

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