Artificial Intelligence for Prosthetics - challenge solutions

1 code implementation • 7 Feb 2019 • Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean F. Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaśkowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy Watson, Marcel Salathé, Sergey Levine, Scott Delp

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.

Imitation Learning reinforcement-learning +1

Learning High-level Representations from Demonstrations

no code implementations • 19 Feb 2018 • Garrett Andersen, Peter Vrancx, Haitham Bou-Ammar

A common approach to HL, is to provide the agent with a number of high-level skills that solve small parts of the overall problem.

Montezuma's Revenge Open-Ended Question Answering +1

Active Exploration for Learning Symbolic Representations

no code implementations • NeurIPS 2017 • Garrett Andersen, George Konidaris

We introduce an online active exploration algorithm for data-efficiently learning an abstract symbolic model of an environment.