Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient Modelfree reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG) often require additional exploration strategies, especially if the actor is of deterministic nature. This work evaluates the use of modelbased trajectory optimization methods used for exploration in Deep Deterministic Policy Gradient when trained on a latent image embedding. In addition, an extension of DDPG is derived using a value function as critic, making use of a learned deep dynamics model to compute the policy gradient. This approach leads to a symbiotic relationship between the deep reinforcement learning algorithm and the latent trajectory optimizer. The trajectory optimizer benefits from the critic learned by the RL algorithm and the latter from the enhanced exploration generated by the planner. The developed methods are evaluated on two continuous control tasks, one in simulation and one in the real world. In particular, a Baxter robot is trained to perform an insertion task, while only receiving sparse rewards and images as observations from the environment.

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.