21 citations · 49 across the 5 of their papers we have counts for
9 papers
Average-Cost Optimality Results for Borel-Space Markov Decision Processes with Universally Measurable Policies
Huizhen Yu
We consider discrete-time Markov Decision Processes with Borel state and action spaces and universally measurable policies. For several long-run average cost criteria, we establish…
Average Cost Optimality Inequality for Markov Decision Processes with Borel Spaces and Universally Measurable Policies
Huizhen Yu
We consider average-cost Markov decision processes (MDPs) with Borel state and action spaces and universally measurable policies. For the nonnegative cost model and an unbounded co…
On the Minimum Pair Approach for Average-Cost Markov Decision Processes with Countable Discrete Action Spaces and Strictly Unbounded Costs
Huizhen Yu
We consider average-cost Markov decision processes (MDPs) with Borel state spaces, countable, discrete action spaces, and strictly unbounded one-stage costs. For the minimum pair a…
On Markov Decision Processes with Borel Spaces and an Average Cost Criterion
Huizhen Yu
We consider average-cost Markov decision processes (MDPs) with Borel state and action spaces and universally measurable policies. For the nonnegative cost model and an unbounded co…
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…
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…