101 citations · 389 across the 29 of their papers we have counts for
4 papers · 1 filter
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…
Optimizing for the Future in Non-Stationary MDPs
Yash Chandak, Georgios Theocharous, Shiv Shankar +3
Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process i…