SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies

1 code implementation • NeurIPS 2019 • Seyed Kamyar Seyed Ghasemipour, Shixiang (Shane) Gu, Richard Zemel

We examine the efficacy of our method on a variety of high-dimensional simulated continuous control tasks and observe that SMILe significantly outperforms Meta-BC.

Continuous Control Few-Shot Learning +3

Particle Gibbs for Infinite Hidden Markov Models

no code implementations • NeurIPS 2015 • Nilesh Tripuraneni, Shixiang (Shane) Gu, Hong Ge, Zoubin Ghahramani

Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric generalization of the classical Hidden Markov Model which can automatically infer the number of hidden states in the system.