<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper presents a complete simulation and reinforcement learning solution to train mobile agents’ strategy of route tracking and avoiding mutual collisions. The aim was to achieve such functionality with limited resources, w.r.t. model input and model size itself. The designed models prove to keep agents safely on the track. Collision avoidance agent’s skills developed in the course of model training are primitive but rational. Small size of the model allows fast training with limited computational resources.</p>
</abstract>

This paper presents a complete simulation and reinforcement learning solution to train mobile agents’ strategy of route tracking and avoiding mutual collisions. The aim was to achieve such functionality with limited resources, w.r.t. model input and model size itself. The designed models prove to keep agents safely on the track. Collision avoidance agent’s skills developed in the course of model training are primitive but rational. Small size of the model allows fast training with limited computational resources.

A Compact Dqn Model for Mobile Agents with Collision Avoidance

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Compact Dqn Model for Mobile Agents with Collision Avoidance"}

This paper presents a complete simulation and reinforcement learning solution to train mobile agents’ strategy of route tracking and avoiding mutual...

A Compact Dqn Model for Mobile Agents with Collision Avoidance

Online veröffentlicht: 26. Jan. 2024

Seitenbereich: 28 - 35

Eingereicht: 11. Apr. 2023

Akzeptiert: 31. Mai 2023

DOI: https://doi.org/10.14313/jamris/2-2023/13

Schlüsselwörter
Q-learning, DQN, Reinforcement learning

© 2023 Mariusz Kamola, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Compact Dqn Model for Mobile Agents with Collision Avoidance

Online veröffentlicht: 26. Jan. 2024

Seitenbereich: 28 - 35

Eingereicht: 11. Apr. 2023

Akzeptiert: 31. Mai 2023

DOI: https://doi.org/10.14313/jamris/2-2023/13

SchlüsselwörterQ-learning, DQN, Reinforcement learning

© 2023 Mariusz Kamola, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Q-learning, DQN, Reinforcement learning