Deep Deterministic Policy Gradient for Navigation of Mobile Robots in Simulated Environments This paper presents a study of a deep reinforcement learning technique that uses a Deep Deterministic Policy Gradient network for application in navigation of mobile robots. In order for the robot to arrive to a target on a map, the network has 10 laser range findings, the previous linear and angular velocity, and relative position and angle of the mobile robot to the target as inputs. As outputs, the network has the linear and angular velocity. From the results analysis, it is possible to conclude that the deep reinforcement learningu0027s algorithms, with continuous actions, are effective for decisionmake of a robotic vehicle. However, it is necessary to create a good reward system for the intelligent agent to accomplish your objectives. This research uses different virtual simulation environments provided by ROBOTIS in the robot simulation software Gazebo in order to test the performance of the algorithm. A supplementary video can be accessed at the following link: https:youtu.beNhGxEC3g7sU. That shows the performance of the proposed system.

Robot Control in Human Environment using Deep Reinforcement Learning and Convolutional Neural Network Deep reinforcement learning (DRL) has been employed in numerous applications where complex decisionmaking is needed. Robot control in a human environment is an example. Such algorithm offers possibilities to achieve endtoend training which learns from image directly. However, training on a physical robotic system under human environments using DRL is inefficient and even dangerous. Several recent works have used simulators for training models before implementing to physical robots. Although simulation provides efficiency to obtain DRL trained models, it poses challenges for the transformation from simulation to reality. Since a human environment is often cluttered, dynamic and complex, the policy trained with simulation images is not applicable for reality. Therefore, in this paper, we propose a DRL method to achieve endtoend training in simulation, as well as to adapt to reality without any further finetune. Firstly, a Deep Deterministic Policy Gradient algorithm (DDPG) is employed to learn policy for robot control. Secondly, a pretrained Convolutional Neural Network algorithm (CNN) is used to visually track the target in image. This technique provides the efficient and safe DRL training in simulation while offering robust application when a real robot is placed in dynamic human environment. Simulation and experiment are conducted for validation and can be seen in the attached video. The results have shown successful demonstration under various complex environments.