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Found 5 papers, 1 papers with code
A Scalable and Parallelizable Digital Twin Framework for Sustainable Sim2Real Transition of Multi-Agent Reinforcement Learning Systems
We introduce AutoDRIVE Ecosystem as an enabling digital twin framework to train, deploy, and transfer cooperative as well as competitive multi-agent reinforcement learning policies from simulation to reality.
An Overview of Automated Vehicle Platooning Strategies
Automated vehicle (AV) platooning has the potential to improve the safety, operational, and energy efficiency of surface transportation systems by limiting or eliminating human involvement in the driving tasks.
Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem
This work presents a modular and parallelizable multi-agent deep reinforcement learning framework for imbibing cooperative as well as competitive behaviors within autonomous vehicles.
Contrastive Representation Disentanglement for Clustering
We conduct a theoretical analysis of the proposed loss and highlight how it assigns different weights to negative samples during the process of disentangling the feature representation.
Multi-level Knowledge Distillation via Knowledge Alignment and Correlation
While it is important to transfer the full knowledge from teacher to student, we introduce the Multi-level Knowledge Distillation (MLKD) by effectively considering both knowledge alignment and correlation.
