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• W2795776994 abstract "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." @default.
• W2795776994 created "2018-04-13" @default.
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• W2795776994 date "2018-01-01" @default.
• W2795776994 modified "2023-10-01" @default.
• W2795776994 title "Learning to Run Challenge Solutions: Adapting Reinforcement Learning Methods for Neuromusculoskeletal Environments" @default.
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• W2795776994 doi "https://doi.org/10.1007/978-3-319-94042-7_7" @default.
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