In this work, we present two Deep Reinforcement Learning (Deep-RL) approaches to enhance the problem of mapless navigation for a terrestrial mobile robot. Our methodology focus on comparing a Deep-RL technique based on the Deep Q-Network (DQN) algorithm with a second one based on the Double Deep Q-Network (DDQN) algorithm. We use 24 laser measurement samples and the relative position and angle of the agent to the target as information for our agents, which provide the actions as velocities for our robot. By using a low-dimensional sensing structure of learning, we show that it is possible to train an agent to perform navigation-related tasks and obstacle avoidance without using complex sensing information. The proposed methodology was successfully used in three distinct simulated environments. Overall, it was shown that Double Deep structures further enhance the problem for the navigation of mobile robots when compared to the ones with simple Q structures.

翻译：本文提出两种深度强化学习方法来改善地面移动机器人的无地图导航问题。我们的方法论重点比较基于深度Q网络（DQN）算法的深度强化学习技术与基于双重深度Q网络（DDQN）算法的第二种技术。我们使用24个激光测量样本以及智能体与目标的相对位置和角度作为智能体的输入信息，智能体则输出速度作为机器人的动作。通过采用低维感知学习结构，我们证明无需复杂的感知信息即可训练智能体执行导航相关任务和避障。所提出的方法在三种不同的模拟环境中成功应用。总体而言，与基于简单Q结构的方案相比，双重深度结构进一步提升了移动机器人的导航性能。