An Actor-Critic Contextual Bandit Algorithm for Personalized Interventions using Mobile Devices

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Robust Actor-Critic Contextual Bandit for Mobile Health (mHealth) Interventions (2018, methodology): “There are nine contextual bandit methods compared in the experiment, including three state-of-the-art methods: (1) Linear Upper Confidence Bound Algorithm (LinUCB) is one of the most famous contextual bandit methods used in the personalized news recommendation [12, 18]; (2) the actor-critic contextu”
Group-driven Reinforcement Learning for Personalized mHealth Intervention (2017, methodology): “…RL [13,14] assumes that all users in the mHealth are completely homogenous: they share all information and learn an identical RL for all the users; (b) the on-policy, online RL [10,9,23] assumes that all users are completely diﬀerent: they share no information and run a separate RL for each user.”