Sökning: "Multi-Armed Bandits"
Visar resultat 1 - 5 av 14 avhandlingar innehållade orden Multi-Armed Bandits.
1. Structured Stochastic Bandits
Sammanfattning : In this thesis we address the multi-armed bandit (MAB) problem with stochastic rewards and correlated arms. Particularly, we investigate the case when the expected rewards are a Lipschitz function of the arm, and the learning to rank problem, as viewed from a MAB perspective. LÄS MER
2. Minimizing Regret in Combinatorial Bandits and Reinforcement Learning
Sammanfattning : This thesis investigates sequential decision making tasks that fall in the framework of reinforcement learning (RL). These tasks involve a decision maker repeatedly interacting with an environment modeled by an unknown finite Markov decision process (MDP), who wishes to maximize a notion of reward accumulated during her experience. LÄS MER
3. Reinforcement Learning and Dynamical Systems
Sammanfattning : This thesis concerns reinforcement learning and dynamical systems in finite discrete problem domains. Artificial intelligence studies through reinforcement learning involves developing models and algorithms for scenarios when there is an agent that is interacting with an environment. LÄS MER
4. Online Learning for Energy Efficient Navigation in Stochastic Transport Networks
Sammanfattning : Reducing the dependence on fossil fuels in the transport sector is crucial to have a realistic chance of halting climate change. The automotive industry is, therefore, transitioning towards an electrified future at an unprecedented pace. LÄS MER
5. Efficient Online Learning under Bandit Feedback
Sammanfattning : In this thesis we address the multi-armed bandit (MAB) problem with stochastic rewards and correlated arms. Particularly, we investigate the case when the expected rewards are a Lipschitz function of the arm and extend these results to bandits with arbitrary structure that is known to the decision maker. LÄS MER