This article describes the use of the Deep Deterministic Policy Gradient network, a deep reinforcement learning algorithm, for mobile robot navigation. The neural network structure has as inputs laser range findings, angular and linear velocities of the robot, and position and orientation of the mobile robot with respect to a goal position. The outputs of the network will be the angular and linear velocities used as control signals for the robot. The experiments demonstrated that deep reinforcement learning’s techniques that uses continuous actions, are efficient for decision-making in a mobile robot. Nevertheless, the design of the reward functions constitutes an important issue in the performance of deep reinforcement learning algorithms. In order to show the performance of the Deep Reinforcement Learning algorithm, we have applied successfully the proposed architecture in simulated environments and in experiments with a real robot.

中文翻译：

---

用于移动机器人导航的深度确定性策略梯度

本文介绍了深度确定性策略梯度网络（一种深度强化学习算法）在移动机器人导航中的使用。神经网络结构具有激光范围发现，机器人的角速度和线速度以及移动机器人相对于目标位置的位置和方向作为输入。网络的输出将是用作机器人控制信号的角速度和线速度。实验表明，采用连续动作的深度强化学习技术对于移动机器人的决策是有效的。尽管如此，奖励函数的设计还是深度强化学习算法性能的重要问题。为了展示深度强化学习算法的性能，