Learning control of underactuated double pendulum with Model-Based Reinforcement Learning

2024/09/09 by Niccolò Turcato, Alberto Dalla Libera, Turcato, Niccolò +7 · 2 citations
Computer Science · Engineering · #Adaptive Dynamic Programming Control #FOS: Computer and information sciences #Iterative Learning Control Systems #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2409.05811

openalex publication_date 2024/09/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand.

Cited by

• VIMPPI: Enhancing Model Predictive Path Integral Control with Variational Integration for Underactuated Systems
• Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks

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