Reinforcemenet learning with Robotics

Abstract— This project applies reinforcement learning algorithms for robotics to learn in real-time. Using an off-policy policy gradient method, we trained an agent that learned how to move the Create2 robot to its docking station. In addition, we investigated the effect of changing one of the sub-goals on the overall behaviour and how it potentially affects the learning process. Our approach outperforms the original implementation in the alignment sub-goal and makes the reward function more interpretable.
Supervised by: Rupam A. Mahmood, RLAI Lab, University of Alberta.
Report - Video

A follow-up project presented at RL4RealLife virtual conference, you can find the presentation here

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