Visualization of Deep Reinforcement Autonomous Aerial Mobility Learning Simulations

TL;DR

This paper demonstrates the visualization of deep reinforcement learning-based autonomous aerial mobility simulations within urban environments using Unity-RL, highlighting effective trajectory and 3D visualization capabilities.

Contribution

It introduces a visualization framework for DRL-based aerial mobility simulations in urban settings, integrating Unity-RL with added environmental complexity.

Findings

Successful implementation of DRL algorithms in urban aerial simulations

Effective trajectory visualization in 3D environments

Enhanced urban environment modeling for aerial mobility simulations

Abstract

This demo abstract presents the visualization of deep reinforcement learning (DRL)-based autonomous aerial mobility simulations. In order to implement the software, Unity-RL is used and additional buildings are introduced for urban environment. On top of the implementation, DRL algorithms are used and we confirm it works well in terms of trajectory and 3D visualization.