mlsanityData to 14 Oct 2026

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

arXiv 1804.00361 ↗First posted 2 Apr 2018cs.LG cs.AI stat.MLcited by 19

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Abstract (arXiv)

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Apr 2018.

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Citing papers per year: 2021: 1; 2022: 10; 2023: 4; 2024: 1; 2025: 2; 2026: 1.

19 papers.

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  1. 2022The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insightscited by 39
  2. 2021Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policiescited by 38
  3. 2022Reward Gaming in Conditional Text Generationcited by 30
  4. 2022DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systemscited by 29
  5. 2022A Survey on Reinforcement Learning Methods in Character Animationcited by 12
  6. 2022Generative GaitNetcited by 11
  7. 2022Perceptual Conversational Head Generation with Regularized Driver and Enhanced Renderercited by 11
  8. 2022ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregationcited by 9
  9. 2022Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Taskscited by 6
  10. 2023Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Modelscited by 5
  11. 2023Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditionscited by 3
  12. 2025Arnold: A multi-task, multi-embodiment muscle transformer policycited by 2
7 more
  1. 2022Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPOcited by 1
  2. 2024Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotioncited by 1
  3. 2022Incorporating Rivalry in Reinforcement Learning for a Competitive Gamecited by 0
  4. 2023DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spacescited by 0
  5. 2023Academic competitionscited by 0
  6. 2025Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learningcited by 0
  7. 2026Where Entropy Is Measured Matters: Policy Geometry in Bounded Continuous-Control PPOcited by 0