  Bayesian strategies for contextual bandits have proved promising in
single-state reinforcement learning tasks by modeling uncertainty using context
information from the environment. In this paper, we propose Greedy Bandits with
Sampled Context (GB-SC), a method for contextual multi-armed bandits to develop
the prior from the context information using Thompson Sampling, and arm
selection using an epsilon-greedy policy. The framework GB-SC allows for
evaluation of context-reward dependency, as well as providing robustness for
partially observable context vectors by leveraging the prior developed. Our
experimental results show competitive performance on the Mushroom environment
in terms of expected regret and expected cumulative regret, as well as insights
on how each context subset affects decision-making.
