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We study the training performance of ROS local planners based onReinforcement Learning (RL), and the trajectories they produce on real-worldrobots. We show that recent enhancements to the Soft Actor Critic (SAC)algorithm such as RAD and DrQ achieve almost perfect training after only 10000episodes. We also observe that on real-world robots the resulting SACPlanner ismore reactive to obstacles than traditional ROS local planners such as DWA.

SACPlanner: Real-World Collision Avoidance with a Soft Actor Critic Local Planner and Polar State Representations