A multi-agent reinforcement learning algorithm with the action preference selection strategy for massive target cooperative search mission planning

Target search is widely applied in military reconnaissance, geological exploration and personnel search and rescue. Most target search algorithms perform well in single target search but are inefficient or even ineffective in multi-target search. To solve the multi-target search problem in an uncertain environment, this paper constructs a multi-agent massive target cooperative search mission planning model and proposes an improved reinforcement learning algorithm using the action preference selection strategy. Based on the Reinforce algorithm, this algorithm solves the problem of invalid searches within the stochastic strategy by changing the preferred action selection method. The proposed method improves the efficiency of multiple agents in the search for targets without collision using a cooperative mechanism and reward rules based on the odor effect. Simulation experiments are conducted in three aspects to verify the effectiveness and robustness of the improved algorithm and compare it with other reinforcement learning algorithms in the field of multi-agent learning. The results demonstrate that the improved algorithm has obvious advantages in terms of mission success rate, target search rate and average search time, and the movement trajectory of multiple agents is more concise.