Path planning of multi-agent systems in unknown environment with neural kernel smoothing and reinforcement learning

In this paper, the classical multi-agent reinforcement learning algorithm is modified such that it does not need the unvisited state. The neural networks and kernel smoothing techniques are applied to approximate greedy actions by estimating the unknown environment. Experimental and simulation results show that the proposed algorithms can generate paths in unknown environment for multiple agents.