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Research questionWhen should soft-arm controllers use centralized versus distributed reinforcement learning under increasing complexity, disturbances, and actuator failures?A single global policy and distributed agent policies may differ in scalability, training efficiency, and recovery when a soft arm becomes more complex or its operation is disrupted. The relevant choice can change between ordinary target reaching, disturbance recovery, and actuator-failure adaptation.
Multi-agent Systems
Reinforcement Learning
Robotics
Latest papersRecent research connected to this question, newest first.A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic ArmsThe evidence comes from simulated Cosserat-rod soft arms in PyElastica with an OpenAI Gym interface. It compares global PPO and MAPPO under identical budgets while varying the number of controlled sections and testing fixed-target reaching, external disturbances, and actuator failures; reported measures include action magnitude, final distance, episode length, and success rate.research paper · Sep 3, 2026
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