# online decision-making

## Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve

We consider the problem of dividing limited resources to individuals arriving over $T$ rounds. Each round has a random number of individuals arrive, and individuals can be characterized by their type (i.e. preferences over the different resources). A …

## Pricing and Optimization in Shared Vehicle Systems: An Approximation Framework

Optimizing shared vehicle systems (bike/scooter/car/ride-sharing) is more challenging compared to traditional resource allocation settings due to the presence of *complex network externalities* -- changes in the demand/supply at any location affect …

## Adaptive Discretization for Model-Based Reinforcement Learning

We introduce the technique of adaptive discretization to design efficient model-based episodic reinforcement learning algorithms in large (potentially continuous) state-action spaces. Our algorithm is based on optimistic one-step value iteration …

## Online Allocation and Pricing: Constant Regret via Bellman Inequalities

We develop a framework for designing simple and efficient policies for a family of online allocation and pricing problems, that includes online packing, budget-constrained probing, dynamic pricing, and online contextual bandits with knapsacks. In …

## Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces

We present an efficient algorithm for model-free episodic reinforcement learning on large (potentially continuous) state-action spaces. Our algorithm is based on a novel Q-learning policy with adaptive data-driven discretization. The central idea is …

## Predict and Match: Prophet Inequalities with Uncertain Supply

We consider a general class of finite-horizon online decision-making problems, where in each period a controller is presented a stochastic arrival and must choose an action from a set of permissible actions, and the final objective depends only on …

## Uniform Loss Algorithms for Online Stochastic Decision-Making With Applications to Bin Packing

We consider a general class of finite-horizon online decision-making problems, where in each period a controller is presented a stochastic arrival and must choose an action from a set of permissible actions, and the final objective depends only on …

## Predict and Match: Prophet Inequalities with Uncertain Supply

We consider a general class of finite-horizon online decision-making problems, where in each period a controller is presented a stochastic arrival and must choose an action from a set of permissible actions, and the final objective depends only on …

## The Bayesian Prophet: A Low-Regret Framework for Online Decision Making

We develop a new framework for designing online policies given access to an oracle providing statistical information about an offline benchmark. Having access to such prediction oracles enables simple and natural Bayesian selection policies, and …

## Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces

We present an efficient algorithm for model-free episodic reinforcement learning on large (potentially continuous) state-action spaces. Our algorithm is based on a novel Q-learning policy with adaptive data-driven discretization. The central idea is …