[N] NIPS 2017 Workshop Call for Papers -- Hierarchical Reinforcement Learning • r/MachineLearning

We invite all researchers to submit their manuscripts for review. Please address questions to: hrlnips2017@gmail.com Reinforcement Learning (RL) has become a powerful tool for tackling complex sequential decision-making problems as demonstrated in high-dimensional robotics or game-playing domains. Nevertheless, modern RL methods have considerable difficulties when facing sparse rewards, long planning horizons, and more generally a scarcity of useful supervision signals. Hierarchical Reinforcement Learning (HRL) is emerging as a key component for finding spatio-temporal abstractions and behavioral patterns that can guide the discovery of useful large-scale control architectures, both for deep-network representations and for analytic and optimal-control methods.