Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning

Abstract

This paper presents a technique that uses feedback images for implementing a robotic task using Deep Reinforcement Learning (DRL). The agent (controller) type is Actor-Critic that uses Temporal-Difference (TD) to gain knowledge about the environment (plant). The representation of the robot is implemented in the V-Rep simulator.