1 citations · 2 across the 6 of their papers we have counts for
6 papers
VL-DPO: Vision-Language-Guided Finetuning for Preference-Aligned Autonomous Driving
Zhefan Xu, Ghassen Jerfel, Marina Haliem +3
The rapid growth of autonomous driving datasets has enabled the scaling of powerful motion forecasting models. While large-scale pretraining provides strong performance, the standa…
Multi-agent Deep Covering Skill Discovery
Jiayu Chen, Marina Haliem, Tian Lan +1
The use of skills (a.k.a., options) can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery m…
AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection
Marina Haliem, Vaneet Aggarwal, Bharat Bhargava
This paper introduces an adaptive model-free deep reinforcement approach that can recognize and adapt to the diurnal patterns in the ride-sharing environment with car-pooling. Deep…
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach
Trevor Bonjour, Marina Haliem, Aala Alsalem +5
Learning to adapt and make real-time informed decisions in a dynamic and complex environment is a challenging problem. Monopoly is a popular strategic board game that requires play…
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning
Kaushik Manchella, Marina Haliem, Vaneet Aggarwal +1
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. A…
A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning
Marina Haliem, Ganapathy Mani, Vaneet Aggarwal +1
Significant development of ride-sharing services presents a plethora of opportunities to transform urban mobility by providing personalized and convenient transportation while ensu…