Have a personal or library account? Click to login
A Compact Dqn Model for Mobile Agents with Collision Avoidance Cover
Open Access
|Jan 2024

References

  1. E. Strubell, Ananya Ganesh, and Andrew McCallum. “Energy and Policy Considerations for Deep Learning in NLP,” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019. doi: 10.48550/arXiv.1906.02243.
  2. Y. Cheng, et al. “Model compression and acceleration for deep neural networks: The principles, progress, and challenges.” IEEE Signal Processing Magazine vol. 35, no. 1, 126–136, 2018. doi: 10.48550/arXiv.1710.09282.
  3. S. Grigorescu, et al. “A survey of deep learning techniques for autonomous driving,” Journal of Field Robotics, vol. 37, no. 3, 362–386, 2020.
  4. M. Hessel, et al. “Rainbow: Combining improvements in deep reinforcement learning,” Proceedings of the AAAI conference on artificial intelligence, vol. 32, no. 1, 2018.
  5. W. Dabney, et al. “Distributional reinforcement learning with quantile regression,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
  6. M. Ahmed, C. P. Lim, and S. Nahavandi. “A Deep Q-Network Reinforcement Learning-Based Model for Autonomous Driving,” 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2021.
  7. J. Carreira, and A. Zisserman. “Quo vadis, action recognition? a new model and the kinetics dataset,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. doi: 10.48550/arXiv.1705.07750.
  8. “How do self-driving cars know their way around without a map?”, https://bigthink.com/technology-innovation/how-do-self-driving-carsknow-their-way-around-without-a-map/ (accessed 2023.03.31).
  9. M. Sewak. “Deep Q Network (DQN), Double DQN, and Dueling DQN: A Step Towards General Arti- ficial Intelligence,” Deep Reinforcement Learning: Frontiers of Artificial Intelligence 2019, 95–108. doi: 10.1007/978-981-13-8285-7_8.
  10. W. Dudek, N. Miguel, and T. Winiarski. “SPSysML: A meta-model for quantitative evaluation of Simulation-Physical Systems,” arXiv preprint arXiv:2303.09565 (2023). doi: 10.48550/arXiv. 2303.09565.
  11. F. S. Chance. “Interception from a Dragonfly Neural Network Model,” International Conference on Neuromorphic Systems, 2020.
  12. “Self-driving cars with Carla and Python,” https://pythonprogramming.net/introductionself-driving-autonomous-cars-carla-python (accessed 2023.03.31).
  13. OpenAI Gym homepage, https://openai.com/research/openai-gym-beta (accessed 2023.03.31).
  14. J. Lin, C. Gan, and S. Han. “TSM: Temporal shift module for efficient video understanding.” Proceedings of the IEEE/CVF international conference on computer vision, 2019.
DOI: https://doi.org/10.14313/jamris/2-2023/13 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649
Language: English
Page range: 28 - 35
Submitted on: Apr 11, 2023
Accepted on: May 31, 2023
Published on: Jan 26, 2024
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2024 Mariusz Kamola, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.