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Found 6 papers, 1 papers with code
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation
We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit.
Minimax-Bayes Reinforcement Learning
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 prior distribution.
Environment Design for Inverse Reinforcement Learning
The task of learning a reward function from expert demonstrations suffers from high sample complexity as well as inherent limitations to what can be learned from demonstrations in a given environment.
ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits
We study the problem of non-stationary dueling bandits and provide the first adaptive dynamic regret algorithm for this problem.
Interactive Inverse Reinforcement Learning for Cooperative Games
How should the first agent act in order to learn the joint reward function as quickly as possible and so that the joint policy is as close to optimal as possible?
On Meritocracy in Optimal Set Selection
Typically, merit is defined with respect to some intrinsic measure of worth.
