1 citations · 2 across the 5 of their papers we have counts for
7 papers · 1 filter
Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning
Reda Ouhamma, Debabrota Basu, Odalric-Ambrym Maillard
We study the problem of episodic reinforcement learning in continuous state-action spaces with unknown rewards and transitions. Specifically, we consider the setting where the rewa…
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty
Hannes Eriksson, Debabrota Basu, Mina Alibeigi +1
In stochastic games with incomplete information, the uncertainty is evoked by the lack of knowledge about a player's own and the other players' types, i.e. the utility function and…
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…
Near-optimal Bayesian Solution For Unknown Discrete Markov Decision Process
Aristide Tossou, Christos Dimitrakakis, Debabrota Basu
We tackle the problem of acting in an unknown finite and discrete Markov Decision Process (MDP) for which the expected shortest path from any state to any other state is bounded by…
Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities
Aristide Tossou, Debabrota Basu, Christos Dimitrakakis
We study model-based reinforcement learning in an unknown finite communicating Markov decision process. We propose a simple algorithm that leverages a variance based confidence int…
Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?
Debabrota Basu, Christos Dimitrakakis, Aristide Tossou
Based on differential privacy (DP) framework, we introduce and unify privacy definitions for the multi-armed bandit algorithms. We represent the framework with a unified graphical…