THIS CHALLENGE IS OVER, BUT THERE IS A NEW ONE AT NIPS 2018

Updates for participants: Please read about the latest changes and the logistics of the second round here and here and here(last update November 6th).

Welcome to Learning to Run, one of the 5 official challenges in the NIPS 2017 Competition Track. In this competition, you are tasked with developing a controller to enable a physiologically-based human model to navigate a complex obstacle course as quickly as possible. You are provided with a human musculoskeletal model and a physics-based simulation environment where you can synthesize physically and physiologically accurate motion. Potential obstacles include external obstacles like steps, or a slippery floor, along with internal obstacles like muscle weakness or motor noise. You are scored based on the distance you travel through the obstacle course in a set amount of time.

Our objectives are to:

bring Deep Reinforcement Learning to solve problems in medicine,

promote open-source tools in RL research (the physics simulator, the RL environment, and the competition platform are all open-source),

encourage RL research in computationally complex environments, with stochasticity and highly-dimensional action spaces.

Follow the instructions in the Getting Started guide in the Dataset section of the challenge and visit our github repo to get started!

First Prize – NVIDIA DGX Station™

NVIDIA DGX Station™ is the Fastest Personal Supercomputer for Researchers and Data Scientists.

Computing support – Amazon AWS cloud credits

Amazon AWS has generously agreed to support participants of the challenge with $30,000 worth of cloud credits. The top 100 performers as per the leaderboard on August 13th, 2017, 23:59:59 UTC, received $300 AWS cloud credits.

Partners

Media