Deep Reinforcement Learning Control of an Autonomous Wheeled Robot in a Challenge Task: Combined Visual and Dynamics Sensoring This paper presents a Deep Reinforcement Learning agent for a 4wheeled rover in a multigoal competition task, under the influence of noisy GPS measurements. A previous related work has implemented a similar agent to the same task using only the raw dynamics measurements as observations. The Proximal Policy Optimization algorithm combined to Universal Value Function Approximators resulted in a system able to successfully overcome very noisy GPS observations and complete the challenge task. This work introduced a frontal camera to add visual input to the rover observations during the task execution. The main change on the algorithm is on the neural networksu0027 architectures, in which a second input layer was added to deal with the image observations. In a few alternate versions of the networks, Long ShortTerm Memory (LSTM) cells were included in the architecture as well. The addition of the camera did not present a significant increase in stability or performance of the network, and the computation time require increased.

On Training Flexible Robots using Deep Reinforcement Learning The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many realworld settings, the uncertainties of the environment, the safety requirements and generalized capabilities that are expected of robots make rigid industrial robots unsuitable. This created great research interest in developing control strategies for flexible robot hardware for which building dynamical models are challenging. In this paper, inspired by the success of deep reinforcement learning (DRL), we systematically study the efficacy of policy search methods using DRL in training flexible robots. Our results indicate that DRL is successfully able to learn efficient and robust policies for complex tasks at various degrees of flexibility. We also note that DRL using Deep Deterministic Policy Gradients can be sensitive to the choice of sensors and adding more informative sensors does not necessarily make the task easier to learn.