Search Results for author:
Found 5 papers, 3 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.
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.
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
In this work, we present a rigorous end-to-end control strategy for autonomous vehicles aimed at minimizing lap times in a time attack racing event.
Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement Learning
This work presents a decentralized motion planning framework for addressing the task of multi-robot navigation using deep reinforcement learning.
Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning
In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning.
