Mixed-experience multi-agent reinforcement learning motion planning method

Abstract

The invention discloses a mixed-experience multi-agent reinforcement learning motion planning method, namely an ME-MADDPG algorithm. According to the method, through MADDPG algorithm training, when a sample is generated, experience is generated through exploration and learning, high-quality experience of successfully planning multiple unmanned aerial vehicles to a target through an artificial potential field method is added, and the two kinds of experience are stored in different experience pools. During training, a neural network collects samples from the two experience pools through dynamic sampling according to the changing probability, state information and environment information of the agents serve as input of the neural network, and the speeds of the multiple agents serve as output. Meanwhile, the neural network is slowly updated in the training process, the training of a multi-agent motion planning strategy is stably completed, and finally, the agents autonomously avoid obstacles in a complex environment and can smoothly reach the respective target positions. According to the method, a motion planning strategy with better stability and adaptability can be efficiently trained in a complex dynamic environment.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and in particular relates to a multi-agent reinforcement learning motion planning method. Background technique [0002] With the vigorous development of scientific theory and science and technology, multi-agent systems are more and more widely used in people's daily production and life. Driving, etc. In these fields, it is necessary to use multi-agent motion planning technology. The multi-agent motion planning problem is a kind of problem of finding the optimal path set of multiple agents from the starting position to the target position without conflicts. How to make the agent avoid obstacles and other agents efficiently and reach the designated area has become a major research problem. [0003] The motion planning methods currently proposed by researchers can be generally divided into global path planning and local path planning. Although global path planning can efficiently and quickl...

AI Technical Summary

Problems solved by technology

[0004] The artificial potential field method has a simple and effective obstacle avoidance planning strategy, but there are problems of local minima and planning that are difficult to apply to dynamic and complex environments; the MADDPG algorithm does not care about the complexity of the environment and has the characteristics of autonomous learning, but it has convergence difficulties and training problems. The problem of poor stability

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