132 citations · 422 across the 45 of their papers we have counts for
51 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…
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Hannes Eriksson, Debabrota Basu, Tommy Tram +2
In this paper, we study the problem of transferring the available Markov Decision Process (MDP) models to learn and plan efficiently in an unknown but similar MDP. We refer to it a…
Environment Design for Inverse Reinforcement Learning
Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis
Learning a reward function from demonstrations suffers from low sample-efficiency. Even with abundant data, current inverse reinforcement learning methods that focus on learning fr…
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…
Interactive Inverse Reinforcement Learning for Cooperative Games
Thomas Kleine Buening, Anne-Marie George, Christos Dimitrakakis
We study the problem of designing autonomous agents that can learn to cooperate effectively with a potentially suboptimal partner while having no access to the joint reward functio…
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling
Hannes Eriksson, Christos Dimitrakakis, Lars Carlsson
We study the problem of performing automated experiment design for drug screening through Bayesian inference and optimisation. In particular, we compare and contrast the behaviour…