51 citations · 233 across the 19 of their papers we have counts for
9 papers · 1 filter
Two geometric input transformation methods for fast online reinforcement learning with neural nets
Sina Ghiassian, Huizhen Yu, Banafsheh Rafiee +1
We apply neural nets with ReLU gates in online reinforcement learning. Our goal is to train these networks in an incremental manner, without the computationally expensive experienc…
On Convergence of some Gradient-based Temporal-Differences Algorithms for Off-Policy Learning
Huizhen Yu
We consider off-policy temporal-difference (TD) learning methods for policy evaluation in Markov decision processes with finite spaces and discounted reward criteria, and we presen…
On Generalized Bellman Equations and Temporal-Difference Learning
Huizhen Yu, A. Rupam Mahmood, Richard S. Sutton
We consider off-policy temporal-difference (TD) learning in discounted Markov decision processes, where the goal is to evaluate a policy in a model-free way by using observations o…
Multi-step Off-policy Learning Without Importance Sampling Ratios
Ashique Rupam Mahmood, Huizhen Yu, Richard S. Sutton
To estimate the value functions of policies from exploratory data, most model-free off-policy algorithms rely on importance sampling, where the use of importance sampling ratios of…
Some Simulation Results for Emphatic Temporal-Difference Learning Algorithms
Huizhen Yu
This is a companion note to our recent study of the weak convergence properties of constrained emphatic temporal-difference learning (ETD) algorithms from a theoretic perspective.…
Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize
Huizhen Yu
We consider the emphatic temporal-difference (TD) algorithm, ETD(), for learning the value functions of stationary policies in a discounted, finite state and action Markov decis…
Emphatic Temporal-Difference Learning
A. Rupam Mahmood, Huizhen Yu, Martha White +1
Emphatic algorithms are temporal-difference learning algorithms that change their effective state distribution by selectively emphasizing and de-emphasizing their updates on differ…
On Convergence of Emphatic Temporal-Difference Learning
Huizhen Yu
We consider emphatic temporal-difference learning algorithms for policy evaluation in discounted Markov decision processes with finite spaces. Such algorithms were recently propose…
A Function Approximation Approach to Estimation of Policy Gradient for POMDP with Structured Policies
Huizhen Yu
We consider the estimation of the policy gradient in partially observable Markov decision processes (POMDP) with a special class of structured policies that are finite-state contro…