关键词: 深度强化学习, 室内搜索, 好奇心

Abstract:

Inview of the low efficiency and low accuracy of indoor random target search by UAV, this paper proposes the deep reinforcement learning algorithm by curiosity-driven exploration based on spatial location annotation. Firstly, it divides the exploration space by regular hexagon, and records the number of the UAV’s visiting in each single area. Then, it generates the internal rewards by the visiting records, which can encourage the UAV to explore new areas continuously and effectively avoid LUAV sinking into local areas. When training the neural network, it uses PPO（Proximal Policy Optimization） algorithm to optimize the parameters, which can find the optimal search strategy faster, avoid the obstacles better, shorten the training period, and improve the search efficiency and accuracy.

Key words: deep reinforcement learning, indoor search, curiosity