26 citations · 64 across the 16 of their papers we have counts for
5 papers · 1 filter
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
Tianyu Li, Bogdan Mazoure, Doina Precup +1
Learning and planning in partially-observable domains is one of the most difficult problems in reinforcement learning. Traditional methods consider these two problems as independen…
Learning Classifiers on Positive and Unlabeled Data with Policy Gradient
Tianyu Li, Chien-Chih Wang, Yukun Ma +4
Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a cl…
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Tianyu Li, Nathan Lambert, Roberto Calandra +2
Learning to locomote to arbitrary goals on hardware remains a challenging problem for reinforcement learning. In this paper, we present a hierarchical learning framework that impro…
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
Rika Antonova, Akshara Rai, Tianyu Li +1
Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work we focus on robotics problems with a budget of only 10-20 trials. This is a ve…
GRP Model for Sensorimotor Learning
Tianyu Li, Bolun Dai
Learning from complex demonstrations is challenging, especially when the demonstration consists of different strategies. A popular approach is to use a deep neural network to perfo…