mlsanityData to 14 Oct 2026

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

arXiv 2409.05811 ↗First posted 9 Sep 2024cs.ROcited by 2

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 13 references that are in the map. First posted on arXiv 9 Sep 2024.

Builds on

2 of 13 references.

References · 2

  1. 2021Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Applicationcited by 10
  2. 2023Learning Control from Raw Position Measurementscited by 3

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Learning global control of underactuated systems with Model-Based Reinforcement Learningcited by 0
  2. 2025Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Taskscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition
  2. 2024Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks
  3. 2025Accelerating Model-Based Reinforcement Learning using Non-Linear Trajectory Optimization
  4. 2024Technical Report on Reinforcement Learning Control on the Lucas-N"ulle Inverted Pendulum
  5. 2024A Pontryagin Perspective on Reinforcement Learning
  6. 2024Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty
  7. 2024Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning
  8. 2026Foundations of Reinforcement Learning and Control:Connections and New Perspectives