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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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Posted-price mechanisms are widely-adopted to decide the price of tasks in popular microtask crowdsourcing. In this paper, we propose a novel postedprice mechanism which not only outperforms existing mechanisms on performance but also avoids their need of a finite price range. The advantages are achieved by converting the pricing problem into a multi-armed bandit problem and designing an optimal algorithm to exploit the unique features of microtask crowdsourcing. We theoretically show the optimality of our algorithm and prove that the performance upper bound can be achieved without the need of a prior price range. We also conduct extensive experiments using real price data to verify the advantages and practicability of our mechanism.