Autonomous navigation is challenging for mobile robots, especially in an unknown environment. Commonly, the robot requires multiple sensors to map the environment, locate itself, and make a plan to reach the target. However, reinforcement learning methods offer an alternative to map-free navigation tasks by learning the optimal actions to take. In this article, deep reinforcement learning agents are implemented using variants of the deep Q networks method, the D3QN and rainbow algorithms, for both the obstacle avoidance and the goal-oriented navigation task. The agents are trained and evaluated in a simulated environment. Furthermore, an analysis of the changes in the behaviour and performance of the agents caused by modifications in the reward function is conducted.

翻译：自主导航对移动机器人而言充满挑战，尤其在未知环境中。通常，机器人需要多传感器来绘制环境地图、定位自身并规划到达目标的路径。然而，强化学习方法通过学习最优动作选择，为无需地图的导航任务提供了替代方案。本文采用深度Q网络方法的变体——D3QN算法和Rainbow算法——实现深度强化学习智能体，分别完成避障和目标导向导航任务。智能体在仿真环境中进行训练和评估。此外，本文还分析了奖励函数调整对智能体行为表现及性能的影响。