Search Results for author: Pradeep Varakantham

Found 15 papers, 1 papers with code

Facilitating human-wildlife cohabitation through conflict prediction

no code implementations22 Sep 2021 Susobhan Ghosh, Pradeep Varakantham, Aniket Bhatkhande, Tamanna Ahmad, Anish Andheria, Wenjun Li, Aparna Taneja, Divy Thakkar, Milind Tambe

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).

CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks

no code implementations8 Jul 2021 Dexun Li, Meghna Lowalekar, Pradeep Varakantham

Influence maximization is the problem of finding a small subset of nodes in a network that can maximize the diffusion of information.

Curriculum Learning

Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare

no code implementations17 May 2021 Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe

In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks.


Competitive Ratios for Online Multi-capacity Ridesharing

no code implementations16 Sep 2020 Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

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

no code implementations13 Sep 2020 Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

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

no code implementations16 Mar 2020 Tanvi Verma, Pradeep Varakantham

For effective matching of resources (e. g., taxis, food, bikes, shopping items) to customer demand, aggregation systems have been extremely successful.

Multi-agent Reinforcement Learning

On Solving Cooperative MARL Problems with a Few Good Experiences

no code implementations22 Jan 2020 Rajiv Ranjan Kumar, Pradeep Varakantham

Unfortunately, neither of these approaches (or their extensions) are able to address the problem of sparse good experiences effectively.

Multi-agent Reinforcement Learning

Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning

no code implementations20 Nov 2019 Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Milind Tambe

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

1 code implementation20 Nov 2019 Sanket Shah, Meghna Lowalekar, Pradeep Varakantham

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

no code implementations13 Dec 2018 Srishti Dhamija, Pradeep Varakantham

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

no code implementations3 Dec 2018 Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar

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

no code implementations27 Mar 2018 Tanvi Verma, Pradeep Varakantham, Hoong Chuin Lau

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.

Fairness Multi-agent Reinforcement Learning

Regret based Robust Solutions for Uncertain Markov Decision Processes

no code implementations NeurIPS 2013 Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

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.

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