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Found 11 papers, 6 papers with code
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need
We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting.
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning
We study a posterior sampling approach to efficient exploration in constrained reinforcement learning.
The Impact of Batch Learning in Stochastic Linear Bandits
Our main theoretical results show that the impact of batch learning is a multiplicative factor of batch size relative to the regret of online behavior.
The Impact of Batch Learning in Stochastic Bandits
We consider a special case of bandit problems, namely batched bandits.
Exploring Offline Policy Evaluation for the Continuous-Armed Bandit Problem
We propose and evaluate an extension of the existing method such that it can be used to evaluate CAB policies.
Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models
In a Bayesian framework for regression trees, Markov Chain Monte Carlo (MCMC) search algorithms are required to generate samples of tree models according to their posterior probabilities.
contextual: Evaluating Contextual Multi-Armed Bandit Problems in R
Over the past decade, contextual bandit algorithms have been gaining in popularity due to their effectiveness and flexibility in solving sequential decision problems---from online advertising and finance to clinical trial design and personalized medicine.
Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream
In marketing we are often confronted with a continuous stream of responses to marketing messages.
StreamingBandit; Experimenting with Bandit Policies
A large number of statistical decision problems in the social sciences and beyond can be framed as a (contextual) multi-armed bandit problem.
Human-Computer Interaction Computers and Society
Lock in Feedback in Sequential Experiments
We often encounter situations in which an experimenter wants to find, by sequential experimentation, $x_{max} = \arg\max_{x} f(x)$, where $f(x)$ is a (possibly unknown) function of a well controllable variable $x$.
Thompson sampling with the online bootstrap
Subsequently, we detail why BTS using the online bootstrap is more scalable than regular Thompson sampling, and we show through simulation that BTS is more robust to a misspecified error distribution.
