3 papers
cs.LG2024
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret
Emilio Jorge, Christos Dimitrakakis, Debabrota Basu
Common assumptions, like linear or RKHS models, and Gaussian or log-concave posteriors over the models, do not explain practical success of RL across a wider range of distributions…
cs.LG2023
Minimax-Bayes Reinforcement Learning
Thomas Kleine Buening, Christos Dimitrakakis, Hannes Eriksson +2
While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prio…
cs.LG2020
Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning
Hannes Eriksson, Emilio Jorge, Christos Dimitrakakis +2
Bayesian reinforcement learning (BRL) offers a decision-theoretic solution for reinforcement learning. While "model-based" BRL algorithms have focused either on maintaining a poste…