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Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
To demonstrate the applicability of our theoretical results, we use them to provide maximal concentration bounds for a large class of reinforcement learning algorithms, including but not limited to on-policy TD-learning with linear function approximation, off-policy TD-learning with generalized importance sampling factors, and $Q$-learning.
Collaboratively Learning the Best Option, Using Bounded Memory
We say an individual learns the best option if eventually (as $t \to \infty$) it pulls only the arm with the highest average reward.
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