Multi-robot path planning (MRPP) enables the robot teams complete the tasks quickly and efficiently without collision. Based on the domestic and foreign research, the mainstream centralized algorithms for path planning have many shortages, so IL-A3C algorithm, which is based on the Dec-POMDP model, is proposed to solve these shortages. Then, the performance of IL-A3C is computed in terms of average path planning length, average path planning time, average collision probability and average planning success rate in different dimensions, and the simulation result shows that IL-A3C performs very well under low obstacle density and can be easily extended to a team of 128 robots. After that, IL-A3C is also compared with the centralized algorithm CBS and A3C algorithm, and the comparison proves that IL-A3C has higher success rate, stronger scalability and stability than CBS and A3C. The conclusion is that IL-A3C can be easily scaled to a large-scale robot team.
Deep Reinforcement Learning (DRL) has been successful applied to a number of fields. In recent years, many scholars have used the DRL algorithms to solve a classic combinatorial optimization problem, i.e. Vehicle Routing Problem (VRP). The scale of the problems that are solved in the literatures is small, thus it is difficult to apply the algorithm into practice where there are many large-scale instances. To solve large-scale VRPs by using DRL, this paper proposes a pre-training mechanism for online shared networks. The graph pointer network under the multi-head attention mechanism is trained in the dual-network reinforcement learning mode. The trained model can be applied to large-scale VRP with 100/300/500 customers within a certain time. The experiments reveal that our algorithm can obtain good solutions in terms of solution quality and offline solution efficiency.
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