In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each team implemented different modifications of the known algorithms by, for example, dividing the task into subtasks, learning low-level control, or by incorporating expert knowledge and using imitation learning.

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In this study, we consider a capacitated vehicle routing problem where the objective function is to minimize the total travel cost. We also consider that the travel costs between the locations are subject to uncertainty, therefore they are expressed as intervals, rather than fixed numbers. The motivation of this study is to solve this problem by using a metaheuristic approach. We base our approach on a variant of ant colony optimization metaheuristic, called ant colony system, which was originally implemented for solving the deterministic version of the problem (i.e. the classical version of the problem without the uncertainty), previously reported in the literature. We modify the algorithm to incorporate a robust optimization methodology, so that the uncertainty on traveling costs can be handled.