Learning global control of underactuated systems with Model-Based Reinforcement Learning

• URL: http://arxiv.org/abs/2504.06721v1
• Date: Wed, 09 Apr 2025 09:20:37 GMT
• Title: Learning global control of underactuated systems with Model-Based Reinforcement Learning
• Authors: Niccolò Turcato, Marco Calì, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres,
• Abstract summary: This paper describes our proposed solution for the third edition of the "AI Olympics with RealAIGym" competition, held at ICRA 2025.<n>We employ Monte-Carlo Probabilistic Inference for Learning Control (MC-PILCO) for its exceptional data efficiency across various low-dimensional robotic tasks.<n>MC-PILCO has won the first two editions of this competition, demonstrating its robustness in both simulated and real-world environments.
• Score: 7.4278142555507065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This short paper describes our proposed solution for the third edition of the "AI Olympics with RealAIGym" competition, held at ICRA 2025. We employed Monte-Carlo Probabilistic Inference for Learning Control (MC-PILCO), an MBRL algorithm recognized for its exceptional data efficiency across various low-dimensional robotic tasks, including cart-pole, ball \& plate, and Furuta pendulum systems. MC-PILCO optimizes a system dynamics model using interaction data, enabling policy refinement through simulation rather than direct system data optimization. This approach has proven highly effective in physical systems, offering greater data efficiency than Model-Free (MF) alternatives. Notably, MC-PILCO has previously won the first two editions of this competition, demonstrating its robustness in both simulated and real-world environments. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand: learning a global policy for the pendubot and acrobot systems.

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