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Thompson Sampling in Partially Observable Contextual Bandits
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual bandits constitute a classical framework for decision-making under uncertainty. In this setting, the goal is to learn the arms of highest reward subject to contextual in…
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual bandits are canonical models for sequential decision-making under uncertainty in environments with time-varying components. In this setting, the expected reward of each…
Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual bandits are widely-used in the study of learning-based control policies for finite action spaces. While the problem is well-studied for bandits with perfectly observed c…
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits
Hongju Park, Mohamad Kazem Shirani Faradonbeh
Contextual multi-armed bandits are classical models in reinforcement learning for sequential decision-making associated with individual information. A widely-used policy for bandit…