<ul><li>This paper presents a solution for the AI Olympics competition held at ICRA 2025.</li><li>The solution utilizes the MC-PILCO algorithm, known for its data efficiency in low-dimensional robotic tasks.</li><li>MC-PILCO optimizes a system dynamics model using interaction data for policy refinement through simulation.</li><li>The algorithm has previously won the first two editions of the competition, demonstrating its effectiveness in both simulated and real-world environments.</li></ul>

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

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