Reinforcement Learning Algorithms Combined with Brain-Inspired Navigation
In response to the low training efficiency,poor generalization ability,and universality of widely used end-to-end rein-forcement learning methods for autonomous navigation of UAV,a brain-inspired navigation model is introduced.Based on the long short-term memory(LSTM)neural network,a brain-inspired cell navigation model is constructed,the self-motion information of the UAV intelligent agent is integrated to encode grid cells and head direction cells,further supplement this information as the state of the deep reinforcement learning algorithm D3QN.The experiments in AirSim simulation environment show that the introduction of the brain-inspired navigation model can effectively improve the training ability of the algorithm and the navigation performance of the UAV intelligent agent.Compared with the original D3QN algorithm,the success rate of reaching the target is increased by 2.54％to 97.11％with the target first fixed.the success rate of reaching the target is 99.45％with the target continued to train after changed.The new target point misses with the success rate of the D3QN of only 11.46％.This indicates that the algorithm effectively improves generalization abilities.
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