no code implementations • 7 Oct 2022 • Jiayu Chen, Marina Haliem, Tian Lan, Vaneet Aggarwal
In this case, we propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 1 Apr 2021 • Marina Haliem, Vaneet Aggarwal, Bharat Bhargava
To mitigate this problem in highly dynamic environments, we (1) adopt an online Dirichlet change point detection (ODCP) algorithm to detect the changes in the distribution of experiences, (2) develop a Deep Q Network (DQN) agent that is capable of recognizing diurnal patterns and making informed dispatching decisions according to the changes in the underlying environment.
no code implementations • 1 Mar 2021 • Trevor Bonjour, Marina Haliem, Aala Alsalem, Shilpa Thomas, Hongyu Li, Vaneet Aggarwal, Mayank Kejriwal, Bharat Bhargava
Monopoly is a popular strategic board game that requires players to make multiple decisions during the game.
no code implementations • 17 Nov 2020 • Kaushik Manchella, Marina Haliem, Vaneet Aggarwal, Bharat Bhargava
The ubiquitous growth of mobility-on-demand services for passenger and goods delivery has brought various challenges and opportunities within the realm of transportation systems.
no code implementations • 5 Oct 2020 • Marina Haliem, Ganapathy Mani, Vaneet Aggarwal, Bharat Bhargava
In this paper, we present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework that (1) dynamically generates optimal routes for each vehicle based on online demand, pricing associated with each ride, vehicle capacities and locations.