1 citations · 1 across the 1 of their papers we have counts for
4 papers
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…
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…
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…
Epistemic Risk-Sensitive Reinforcement Learning
Hannes Eriksson, Christos Dimitrakakis
We develop a framework for interacting with uncertain environments in reinforcement learning (RL) by leveraging preferences in the form of utility functions. We claim that there is…