<p>Poster presented at Reinforcement learning and decision making conference in 2015 in Edmonton, Canada. This work is closely related to the following article: Stojic, H., Analytis, P. P., & Speekenbrink, M. (2015). Human behavior in contextual multi-armed bandit problems. Proceedings of the 37th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.</p
The multi-armed bandit framework can be motivated by any problem where there is an abundance of choi...
Research in cognitive psychology regarding sequential decision-making usually involves tasks where a...
International audienceThis paper presents a new reinforcement learning (RL) algorithm called Bellman...
<p>Poster presented at the Society of Neuroeconomics Annual Meeting in 2015 in Miami. This work is c...
<p>Presentation slides from a talk given at Barcelona systems neuroscience meeting (BARCCSYN) in 201...
<p>This is raw data from the experiment described in the following article: Stojic, H., Analytis, P....
In real-life decision environments people learn from their di-rect experience with alternative cours...
Presented as part of the ARC11 lecture on October 30, 2017 at 10:00 a.m. in the Klaus Advanced Compu...
The authors introduce the contextual multi-armed bandit task as a framework to investigate learning ...
The bandit problem models a sequential decision process between a player and an environment. In the ...
Abstract—We present a formal model of human decision-making in explore-exploit tasks using the conte...
Modelling human behaviour is a complicated task that requires the use of knowledge from many domains...
The multi-armed bandit problem is a statistical decision model of an agent trying to optimize his de...
International audienceThis paper presents a new contextual bandit algorithm, NeuralBandit, which doe...
This thesis considers the multi-armed bandit (MAB) problem, both the traditional bandit feedback and...
The multi-armed bandit framework can be motivated by any problem where there is an abundance of choi...
Research in cognitive psychology regarding sequential decision-making usually involves tasks where a...
International audienceThis paper presents a new reinforcement learning (RL) algorithm called Bellman...
<p>Poster presented at the Society of Neuroeconomics Annual Meeting in 2015 in Miami. This work is c...
<p>Presentation slides from a talk given at Barcelona systems neuroscience meeting (BARCCSYN) in 201...
<p>This is raw data from the experiment described in the following article: Stojic, H., Analytis, P....
In real-life decision environments people learn from their di-rect experience with alternative cours...
Presented as part of the ARC11 lecture on October 30, 2017 at 10:00 a.m. in the Klaus Advanced Compu...
The authors introduce the contextual multi-armed bandit task as a framework to investigate learning ...
The bandit problem models a sequential decision process between a player and an environment. In the ...
Abstract—We present a formal model of human decision-making in explore-exploit tasks using the conte...
Modelling human behaviour is a complicated task that requires the use of knowledge from many domains...
The multi-armed bandit problem is a statistical decision model of an agent trying to optimize his de...
International audienceThis paper presents a new contextual bandit algorithm, NeuralBandit, which doe...
This thesis considers the multi-armed bandit (MAB) problem, both the traditional bandit feedback and...
The multi-armed bandit framework can be motivated by any problem where there is an abundance of choi...
Research in cognitive psychology regarding sequential decision-making usually involves tasks where a...
International audienceThis paper presents a new reinforcement learning (RL) algorithm called Bellman...