Contextual Multi-Armed Bandit Problems in Reinforcement Learning
Too Long; Didn't Read
This article dives into context-based multi-armed bandit problems in reinforcement learning, where the reward depends on the context. We discussed and implemented three different algorithms: LinUCB, Decision Trees, and Neural Networks to solve these problems, highlighting their unique strengths and considerations. Although we didn't explicitly compare their performance, we emphasized the importance of choosing the right approach based on the characteristics of the problem at hand.