PATH PLANNING FOR AGENT BASED ON IMPROVED LAYERED DQN ALGORITHM

In order to solve the problems that the convergence speed is slow and it is difficult for Q value to describe the action accurately when an agent uses DQN algorithm in the process of path planning,a layered DQN algorithm optimized by the model structure of DQN is proposed.The excitation layer and the action layer built by the algorithm were superimposed to generate a more accurate Q value,which was used to select the optimal action and make the anti-interference ability of the whole network stronger.The simulation results show that the agent using layered DQN algorithm has a faster convergence speed,thus verifying the feasibility and effectiveness of the algorithm.