Weighted area coverage of maritime joint search and rescue based on multi-agent reinforcement learning

Maritime search and rescue is the last line of defense to ensure the safety of life and property at sea. To solve the problem of cooperative planning of regional coverage task for multiple search and rescue equipment in the actual search and rescue process. Firstly, we propose a weighted region model mapped by the Monte Carlo drift prediction model. And then we use three reinforcement learning algorithms (Q-learning, Sarsa, Sarsa (lambda)) for comparative experiments. Finally, three algorithms are evaluated per the coverage and repetition rate, and the experimental results show that Sarsa coverage rate is higher and repetition rate is lower. We have solved the weighted area coverage problem in the real search and rescue process, greatly improving the decision-making efficiency of the system, and making the search with the highest probability, high search coverage and low repetition rate, which has extremely high practical value.