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

Learning global control of underactuated systems with Model-Based Reinforcement Learning Niccol ` o Turcato 1, Marco Cal ` ı 1, Alberto Dalla Libera 1, Giulio Giacomuzzo 1, Ruggero Carli 1 and Diego Romeres 2 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. 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.