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Found 15 papers, 2 papers with code
Best Arm Identification with Resource Constraints
Motivated by the cost heterogeneity in experimentation across different alternatives, we study the Best Arm Identification with Resource Constraints (BAIwRC) problem.
Online Resource Allocation: Bandits feedback and Advice on Time-varying Demands
Finally, we adapt our model to a network revenue management problem, and numerically demonstrate that our algorithm can still performs competitively compared to existing baselines.
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits
We study the Pareto frontier of two archetypal objectives in multi-armed bandits, namely, regret minimization (RM) and best arm identification (BAI) with a fixed horizon.
Reinforcement Learning with Ex-Post Max-Min Fairness
We consider reinforcement learning with vectorial rewards, where the agent receives a vector of $K\geq 2$ different types of rewards at each time step.
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
When the amount of corruptions per step (CPS) is below a threshold, PSS($u$) identifies the best arm or item with probability tending to $1$ as $T\rightarrow \infty$.
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under drifting non-stationarity, i. e., both the reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets.
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
Finally, extensive numerical simulations corroborate the efficacy of CascadeBAI as well as the tightness of our upper bound on its time complexity.
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives
We consider an agent who is involved in an online Markov decision process, and receives a vector of outcomes every round.
Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism
Notably, the interplay between endogeneity and exogeneity presents a unique challenge, absent in existing (stationary and non-stationary) stochastic online learning settings, when we apply the conventional Optimism in Face of Uncertainty principle to design algorithms with provably low dynamic regret for RL in drifting MDPs.
Exploration-Exploitation Trade-off in Reinforcement Learning on Online Markov Decision Processes with Global Concave Rewards
In our general setting where a stationary policy could have multiple recurrent classes, the agent faces a subtle yet consequential trade-off in alternating among different actions for balancing the vectorial outcomes.
Hedging the Drift: Learning to Optimize under Non-Stationarity
Boosted by the novel bandit-over-bandit framework that adapts to the latent changes, we can further enjoy the (nearly) optimal dynamic regret bounds in a (surprisingly) parameter-free manner.
Inventory Balancing with Online Learning
We overcome both the challenges of model uncertainty and customer heterogeneity by judiciously synthesizing two algorithmic frameworks from the literature: inventory balancing, which "reserves" a portion of each resource for high-reward customer types which could later arrive, and online learning, which shows how to "explore" the resource consumption distributions of each customer type under different actions.
Learning to Optimize under Non-Stationarity
We introduce algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary linear stochastic bandit setting.
Thompson Sampling Algorithms for Cascading Bandits
While Thompson sampling (TS) algorithms have been shown to be empirically superior to Upper Confidence Bound (UCB) algorithms for cascading bandits, theoretical guarantees are only known for the latter.
Assortment Optimization under Unknown MultiNomial Logit Choice Models
We first propose an efficient online policy which incurs a regret $\tilde{O}(T^{2/3})$, where $T$ is the number of customers in the sales horizon.
