  In this paper, it has attempted to use Reinforcement learning to model the
proper dosage of Warfarin for patients.The paper first examines two baselines:
a fixed model of 35 mg/week dosages and a linear model that relies on patient
data. We implemented a LinUCB bandit that improved performance measured on
regret and percent incorrect. On top of the LinUCB bandit, we experimented with
online supervised learning and reward reshaping to boost performance. Our
results clearly beat the baselines and show the promise of using multi-armed
bandits and artificial intelligence to aid physicians in deciding proper
dosages.
