Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning

05/30/2017
by   Hamid Mirzaei, et al.
0

Recent advances in combining deep learning and Reinforcement Learning have shown a promising path for designing new control agents that can learn optimal policies for challenging control tasks. These new methods address the main limitations of conventional Reinforcement Learning methods such as customized feature engineering and small action/state space dimension requirements. In this paper, we leverage one of the state-of-the-art Reinforcement Learning methods, known as Trust Region Policy Optimization, to tackle intersection management for autonomous vehicles. We show that using this method, we can perform fine-grained acceleration control of autonomous vehicles in a grid street plan to achieve a global design objective.

READ FULL TEXT
research
03/30/2020

Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties

This paper presents a novel model-reference reinforcement learning contr...
research
05/02/2017

Navigating Intersections with Autonomous Vehicles using Deep Reinforcement Learning

Providing an efficient strategy to navigate safely through unsignaled in...
research
05/31/2022

Robust Longitudinal Control for Vehicular Autonomous Platoons Using Deep Reinforcement Learning

In the last few years, researchers have applied machine learning strateg...
research
08/14/2020

Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning

Decision-making module enables autonomous vehicles to reach appropriate ...
research
10/24/2022

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning

Policies produced by deep reinforcement learning are typically character...
research
01/15/2022

Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management

Shared Automated Vehicles (SAVs) Fleets companies are starting pilot pro...
research
05/05/2022

HARL: A Novel Hierachical Adversary Reinforcement Learning for Automoumous Intersection Management

As an emerging technology, Connected Autonomous Vehicles (CAVs) are beli...

Please sign up or login with your details

Forgot password? Click here to reset