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

2023/03/21 by Khaled Nakhleh, Minahil Raza, Nakhleh, Khaled +15 · 1 citation
Computer Science · #Artificial Intelligence in Games #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robotic Path Planning Algorithms #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2303.11801

openalex publication_date 2023/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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

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• Effective Reinforcement Learning Control using Conservative Soft Actor-Critic

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