4 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
Adaptive Belief Discretization for POMDP Planning
Divya Grover, Christos Dimitrakakis
Partially Observable Markov Decision Processes (POMDP) is a widely used model to represent the interaction of an environment and an agent, under state uncertainty. Since the agent…
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…
Bayesian Reinforcement Learning via Deep, Sparse Sampling
Divya Grover, Debabrota Basu, Christos Dimitrakakis
We address the problem of Bayesian reinforcement learning using efficient model-based online planning. We propose an optimism-free Bayes-adaptive algorithm to induce deeper and spa…