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 …
A core tension in the operations of online marketplaces is between segmentation (wherein platforms can increase revenue by segmenting the market into ever smaller sub-markets) and thickness (wherein the size of the sub-market affects the utility …
A core tension in the operations of online marketplaces is between segmentation (wherein platforms can increase revenue by segmenting the market into ever smaller sub-markets) and thickness (wherein the size of the sub-market affects the utility …
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 …
Ridesharing platforms such as Didi, Lyft, Ola and Uber are increasingly important components of the transportation infrastructure. However, our understanding of their design and operations, and their effect on society at large, is not yet well …
We study the design of state dependent control for a closed queueing network model, inspired by shared transportation systems such as ridesharing. In particular, we focus on the design of assignment policies, wherein the platform can choose which …
Non-monetary mechanisms for repeated allocation and decision-making are gaining widespread use in many real-world settings. Our aim in this work is to study the efficiency and incentive properties of simple mechanisms based on artificial currencies …
Optimizing shared vehicle systems (bike-sharing/car-sharing/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 …
We study the design of mechanisms without money for repeated allocation of resources among competing agents. Such mechanisms are gaining widespread use in allocating physical and/or computing resources in universities and companies, and also …
A common phenomena in modern recommendation systems is the use of feedback from one user to infer the 'value' of an item to other users. This results in an exploration vs. exploitation trade-off, in which items of possibly low value have to be …