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09/10/2023
Convex Q Learning in a Stochastic Environment: Extended Version
The paper introduces the first formulation of convex Q-learning for Mark...
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09/06/2023
The Curse of Memory in Stochastic Approximation: Extended Version
Theory and application of stochastic approximation (SA) has grown within...
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07/05/2023
Stability of Q-Learning Through Design and Optimism
Q-learning has become an important part of the reinforcement learning to...
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10/17/2022
Sufficient Exploration for Convex Q-learning
In recent years there has been a collective research effort to find new ...
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10/14/2022
Model-Free Characterizations of the Hamilton-Jacobi-Bellman Equation and Convex Q-Learning in Continuous Time
Convex Q-learning is a recent approach to reinforcement learning, motiva...
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10/27/2021
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
The paper concerns convergence and asymptotic statistics for stochastic ...
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09/30/2020
Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation
The ODE method has been a workhorse for algorithm design and analysis si...
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02/07/2020
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation
This paper concerns error bounds for recursive equations subject to Mark...
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10/11/2019
Zap Q-Learning With Nonlinear Function Approximation
The Zap stochastic approximation (SA) algorithm was introduced recently ...
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09/17/2018
Zap Meets Momentum: Stochastic Approximation Algorithms with Optimal Convergence Rate
There are two well known Stochastic Approximation techniques that are kn...
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08/05/2018
