Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of Terrestrial Mobile Robots
<|reference_start|>Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of Terrestrial Mobile Robots: This paper introduces novel deep reinforcement learning (Deep-RL) techniques using parallel distributional actor-critic networks for navigating terrestrial mobile robots. Our approaches use laser range findings, relative distance, and angle to the target to guide the robot. We trained agents in the Gazebo simulator and deployed them in real scenarios. Results show that parallel distributional Deep-RL algorithms enhance decision-making and outperform non-distributional and behavior-based approaches in navigation and spatial generalization.<|reference_end|>
@article{kich2024parallel,
    title={Parallel Distributional Deep Reinforcement Learning for Mapless
  Navigation of Terrestrial Mobile Robots},
    author={Victor Augusto Kich, Alisson Henrique Kolling, Junior Costa de Jesus,
  Gabriel V. Heisler, Hiago Jacobs, Jair Augusto Bottega, Andr'e L. da S.
  Kelbouscas, Akihisa Ohya, Ricardo Bedin Grando, Paulo Lilles Jorge Drews-Jr,
  Daniel Fernando Tello Gamarra},
    journal={arXiv preprint arXiv:2408.05744},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05744},
    primaryClass={cs.RO}
}