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

## TL;DR

This paper applies advanced deep reinforcement learning, specifically Trust Region Policy Optimization, to optimize fine-grained acceleration control for autonomous vehicles at intersections, aiming for improved traffic management.

## Contribution

It introduces a novel application of TRPO for intersection management, enabling precise acceleration control in autonomous vehicle coordination.

## Key findings

- Effective fine-grained acceleration control demonstrated
- Improved intersection throughput and safety potential
- Utilizes state-of-the-art reinforcement learning method

## Abstract

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.