3 citations · 4 across the 6 of their papers we have counts for
6 papers
Average-reward reinforcement learning in semi-Markov decision processes via relative value iteration
Huizhen Yu, Yi Wan, Richard S. Sutton
This paper applies the authors' recent results on asynchronous stochastic approximation (SA) in the Borkar-Meyn framework to reinforcement learning in average-reward semi-Markov de…
Asynchronous Stochastic Approximation with Applications to Average-Reward Reinforcement Learning
Huizhen Yu, Yi Wan, Richard S. Sutton
This paper investigates the stability and convergence properties of asynchronous stochastic approximation (SA) algorithms, with a focus on extensions relevant to average-reward rei…
On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes
Yi Wan, Huizhen Yu, Richard S. Sutton
This paper analyzes reinforcement learning (RL) algorithms for Markov decision processes (MDPs) under the average-reward criterion. We focus on Q-learning algorithms based on relat…
Reward Centering
Abhishek Naik, Yi Wan, Manan Tomar +1
We show that discounted methods for solving continuing reinforcement learning problems can perform significantly better if they center their rewards by subtracting out the rewards'…
A Note on Stability in Asynchronous Stochastic Approximation without Communication Delays
Huizhen Yu, Yi Wan, Richard S. Sutton
In this paper, we study asynchronous stochastic approximation algorithms without communication delays. Our main contribution is a stability proof for these algorithms that extends…
Iterative Option Discovery for Planning, by Planning
Kenny Young, Richard S. Sutton
Discovering useful temporal abstractions, in the form of options, is widely thought to be key to applying reinforcement learning and planning to increasingly complex domains. Build…