The paper introduces the first formulation of convex Q-learning for Mark...
Theory and application of stochastic approximation (SA) has grown within...
Q-learning has become an important part of the reinforcement learning to...
In recent years there has been a collective research effort to find new
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Convex Q-learning is a recent approach to reinforcement learning, motiva...
The paper concerns convergence and asymptotic statistics for stochastic
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The ODE method has been a workhorse for algorithm design and analysis si...
This paper concerns error bounds for recursive equations subject to Mark...
The Zap stochastic approximation (SA) algorithm was introduced recently ...
There are two well known Stochastic Approximation techniques that are kn...
A new methodology is presented for the construction of control variates ...