Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
This work addresses the challenge of applying reinforcement learning to neuromusculoskeletal environments, but it is incremental as it adapts existing methods without introducing new paradigms.
The paper tackled the problem of controlling a musculoskeletal model to run fast through an obstacle course in the NIPS 2017 Learning to Run challenge, presenting eight solutions based on deep reinforcement learning methods like DDPG, PPO, and TRPO, with teams implementing different modifications of these known algorithms.
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.