101 citations · 146 across the 4 of their papers we have counts for
5 papers
Finite-Sample Analysis of Proximal Gradient TD Algorithms
Bo Liu, Ji Liu, Mohammad Ghavamzadeh +2
In this paper, we analyze the convergence rate of the gradient temporal difference learning (GTD) family of algorithms. Previous analyses of this class of algorithms use ODE techni…
Regularized Off-Policy TD-Learning
Bo Liu, Sridhar Mahadevan, Ji Liu
We present a novel regularized off-policy convergent TD-learning method (termed RO-TD), which is able to learn sparse representations of value functions with low computationa…
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity
Bo Liu, Ian Gemp, Mohammad Ghavamzadeh +3
In this paper, we introduce proximal gradient temporal difference learning, which provides a principled way of designing and analyzing true stochastic gradient temporal difference…
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning
Bo Liu, Luwan Zhang, Ji Liu
Dantzig Selector (DS) is widely used in compressed sensing and sparse learning for feature selection and sparse signal recovery. Since the DS formulation is essentially a linear pr…
Sparse Q-learning with Mirror Descent
Sridhar Mahadevan, Bo Liu
This paper explores a new framework for reinforcement learning based on online convex optimization, in particular mirror descent and related algorithms. Mirror descent can be viewe…