Bandit Linear Control

Reinforcement learning studies sequential decision making problems where a learning agent repeatedly interacts with an environment and aims to improve her strategy over time based on the received feedback. One of the most fundamental tradeoffs in reinforcement learning theory is the exploration vs. exploitation tradeoff, that arises whenever the learner observes only partial feedback after each of her decisions, thus having to balance between exploring new strategies and exploiting those that are already known to perform well. The most basic and well-studied form of partial feedback is the so-called "bandit" feedback, where the learner only observes the cost of her chosen action on each decision round, while obtaining no information about the performance of other actions. Traditionally, the environment dynamics in reinforcement learning are modeled as a Markov Decision Process (MDP) with a finite number of possible states and actions. The MDP model has been studied and analyzed in numerous different settings and under various assumptions on the transition parameters, the nature of the reward functions, and the feedback model. Recently, a particular focus has been given to continuous state-action MDPs, and in particular, to a specific family of models in classic control where the state transition function is linear.