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Found 10 papers, 0 papers with code
Generalizing Cooperative Eco-driving via Multi-residual Task Learning
To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning.
Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective
To overcome these challenges and enhance real-world applicability in near-term, we propose a model-free approach employing deep reinforcement learning (DRL) for designing CAV control strategies, showing its reduced overhead in designing and greater scalability and performance compared to model-based methods.
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning
We show that in comparison to evaluating DRL methods on select MDP instances, evaluating the MDP family often yields a substantially different relative ranking of methods, casting doubt on what methods should be considered state-of-the-art.
Learning Eco-Driving Strategies at Signalized Intersections
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions.
The Braess Paradox in Dynamic Traffic
The Braess's Paradox (BP) is the observation that adding one or more roads to the existing road network will counter-intuitively increase traffic congestion and slow down the overall traffic flow.
Fleet management for ride-pooling with meeting points at scale: a case study in the five boroughs of New York City
To the best of our knowledge, STaRS+ is the first study on the RPMP that can solve large-scale instances on the order of the entire NYC metro area.
Legal Document Retrieval using Document Vector Embeddings and Deep Learning
The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semantic similarity measures into the information retrieval process.
Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings
With the use of word embeddings in the field of natural language processing, it became a popular topic due to its ability to cope up with semantic sensitivity.
Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings
Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks.
Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity
Semantic similarity measures are an important part in Natural Language Processing tasks.
