Welcome to AI for Prosthetics challenge, one of the official challenges in the NIPS 2018 Competition Track. In this competition, you are tasked with developing a controller to enable a physiologically-based human model with a prosthetic leg to navigate. You are provided with a human musculoskeletal model, a physics-based simulation environment where you can synthesize physically and physiologically accurate motion, and datasets of normal gait kinematics. You are scored based on how well your agent adapts to requested velocity vector changing in real 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.
Visit our github repo to get started!