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Found 5 papers, 3 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.
Learning Optimal Bidding Strategy: Case Study in E-Commerce Advertising
Although the bandits framework is a classical and well-suited approach for optimal bidding strategies in sponsored search auctions, industrial attempts are rarely documented.
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.
