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Found 15 papers, 1 papers with code
Facilitating human-wildlife cohabitation through conflict prediction
With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (animal and human) and livelihoods (economic).
Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health
Our second major contribution is evaluation of our RMAB system in collaboration with an NGO, via a real-world service quality improvement study.
CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks
Influence maximization is the problem of finding a small subset of nodes in a network that can maximize the diffusion of information.
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare
In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks.
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes
India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100, 000 live births.
Competitive Ratios for Online Multi-capacity Ridesharing
The desired matching between resources and request groups is constrained by the edges between requests and request groups in this tripartite graph (i. e., a request can be part of at most one request group in the final assignment).
Zone pAth Construction (ZAC) based Approaches for Effective Real-Time Ridesharing
This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles.
Value Variance Minimization for Learning Approximate Equilibrium in Aggregation Systems
For effective matching of resources (e. g., taxis, food, bikes, shopping items) to customer demand, aggregation systems have been extremely successful.
On Solving Cooperative MARL Problems with a Few Good Experiences
Unfortunately, neither of these approaches (or their extensions) are able to address the problem of sparse good experiences effectively.
Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning
To solve the online problem with a hard bound on risk, we formulate it as a Reinforcement Learning (RL) problem with constraints on the action space (hard bound on risk).
Neural Approximate Dynamic Programming for On-Demand Ride-Pooling
This is because even a myopic assignment in ride-pooling involves considering what combinations of passenger requests that can be assigned to vehicles, which adds a layer of combinatorial complexity to the ToD problem.
TuSeRACT: Turn-Sample-Based Real-Time Traffic Signal Control
To ensure real-time responsiveness in the presence of turn-induced uncertainty, SURTRAC computes schedules which minimize the delay for the expected turn movements as opposed to minimizing the expected delay under turn-induced uncertainty.
Resource Constrained Deep Reinforcement Learning
However, existing Deep RL methods are unable to handle combinatorial action spaces and constraints on allocation of resources.
Entropy based Independent Learning in Anonymous Multi-Agent Settings
A key characteristic of the domains of interest is that the interactions between individuals are anonymous, i. e., the outcome of an interaction (competing for demand) is dependent only on the number and not on the identity of the agents.
Regret based Robust Solutions for Uncertain Markov Decision Processes
Most robust optimization approaches for these problems have focussed on the computation of {\em maximin} policies which maximize the value corresponding to the worst realization of the uncertainty.