# Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning

arXiv: https://arxiv.org/abs/1804.00198 · HTML: https://mlsanity.com/paper/1804.00198/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/2101.11071/) (2021)
- [A Survey on Physics Informed Reinforcement Learning: Review and Open Problems](https://mlsanity.com/paper/2309.01909/) (2023)
- [Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark](https://mlsanity.com/paper/2103.15332/) (2021)
- [A Survey on Reinforcement Learning Methods in Character Animation](https://mlsanity.com/paper/2203.04735/) (2022)
- [Scalable Exploration for High-Dimensional Continuous Control via Value-Guided Flow](https://mlsanity.com/paper/2601.19707/) (2026)
- [Arnold: A multi-task, multi-embodiment muscle transformer policy](https://mlsanity.com/paper/2508.18066/) (2025)

## Abstract (arXiv)

Synthesizing physiologically-accurate human movement in a variety of conditions can help practitioners plan surgeries, design experiments, or prototype assistive devices in simulated environments, reducing time and costs and improving treatment outcomes. Because of the large and complex solution spaces of biomechanical models, current methods are constrained to specific movements and models, requiring careful design of a controller and hindering many possible applications. We sought to discover if modern optimization methods efficiently explore these complex spaces. To do this, we posed the problem as a competition in which participants were tasked with developing a controller to enable a physiologically-based human model to navigate a complex obstacle course as quickly as possible, without using any experimental data. They were provided with a human musculoskeletal model and a physics-based simulation environment. In this paper, we discuss the design of the competition, technical difficulties, results, and analysis of the top controllers. The challenge proved that deep reinforcement learning techniques, despite their high computational cost, can be successfully employed as an optimization method for synthesizing physiologically feasible motion in high-dimensional biomechanical systems.