Reinforcement learning with parameterized action space and sparse reward for UAV navigation

Shiying Feng, Xiaofeng Li, Lu Ren, Shuiqing Xu
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Abstract

Autonomous navigation of unmanned aerial vehicles (UAVs) is widely used in building rescue systems. As the complexity of the task increases, traditional methods based on environment models are hard to apply. In this paper, a reinforcement learning (RL) algorithm is proposed to solve the UAV navigation problem. The UAV navigation task is modeled as a Markov Decision Process (MDP) with parameterized actions. In addition, the sparse reward problem is also taken into account. To address these issues, we develop the HER-MPDQN by combining Multi-Pass Deep Q-Network (MP-DQN) and Hindsight Experience Replay (HER). Two UAV navigation simulation environments with progressive difficulty are constructed to evaluate our method. The results show that HER-MPDQN outperforms other baselines in relatively simple tasks. Especially for complex tasks involving relay operations, only our method can achieve satisfactory performance.
基于参数化动作空间和稀疏奖励的无人机导航强化学习
无人机的自主导航在建筑救援系统中有着广泛的应用。随着任务复杂性的增加,传统的基于环境模型的方法难以应用。针对无人机导航问题,提出了一种强化学习(RL)算法。将无人机导航任务建模为具有参数化动作的马尔可夫决策过程。此外,还考虑了稀疏奖励问题。为了解决这些问题,我们将多通道深度q网络(MP-DQN)和事后经验回放(HER)相结合,开发了HER- mpdqn。构建了两个渐进式难度的无人机导航仿真环境,对该方法进行了验证。结果表明HER-MPDQN在相对简单的任务中优于其他基准。特别是对于涉及继电器操作的复杂任务,只有我们的方法才能达到令人满意的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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