{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T05:26:53Z","timestamp":1782365213013,"version":"3.54.5"},"reference-count":58,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ANID (National Research and Development Agency of Chile) under project Fondecyt Iniciaci\u00f3n en Investigaci\u00f3n 2023","award":["11230962"],"award-info":[{"award-number":["11230962"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Autonomous navigation in mining environments is challenged by complex wheel\u2013terrain interaction, traction losses caused by slip dynamics, and sensor limitations. This paper investigates the effectiveness of Deep Reinforcement Learning (DRL) techniques for the trajectory tracking control of skid-steer mobile robots operating under terra-mechanical constraints. Four state-of-the-art DRL algorithms, i.e., Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor\u2013Critic (SAC), are selected to evaluate their ability to generate stable and adaptive control policies under varying environmental conditions. To address the inherent partial observability in real-world navigation, this study presents an original approach that integrates Long Short-Term Memory (LSTM) networks into DRL-based controllers. This allows control agents to retain and leverage temporal dependencies to infer unobservable system states. The developed agents were trained and tested in simulations and then assessed in field experiments under uneven terrain and dynamic model parameter changes that lead to traction losses in mining environments, targeting various trajectory tracking tasks, including lemniscate and squared-type reference trajectories. This contribution strengthens the robustness and adaptability of DRL agents by enabling better generalization of learned policies compared with their baseline counterparts, while also significantly improving trajectory tracking performance. In particular, LSTM-based controllers achieved reductions in tracking errors of 10%, 74%, 21%, and 37% for DDPG-LSTM, PPO-LSTM, TD3-LSTM, and SAC-LSTM, respectively, compared with their non-recurrent counterparts. Furthermore, DDPG-LSTM and TD3-LSTM reduced their control effort through the total variation in control input by 15% and 20% compared with their respective baseline controllers, respectively. Findings from this work provide valuable insights into the role of memory-augmented reinforcement learning for robust motion control in unstructured and high-uncertainty environments.<\/jats:p>","DOI":"10.3390\/robotics14060074","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T07:46:26Z","timestamp":1748504786000},"page":"74","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["LSTM-Enhanced Deep Reinforcement Learning for Robust Trajectory Tracking Control of Skid-Steer Mobile Robots Under Terra-Mechanical Constraints"],"prefix":"10.3390","volume":"14","author":[{"given":"Jose Manuel","family":"Alcayaga","sequence":"first","affiliation":[{"name":"Departamento de Ingenier\u00eda de Sistemas y Computaci\u00f3n, Universidad Cat\u00f3lica del Norte, Antofagasta 1249004, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7101-0319","authenticated-orcid":false,"given":"Oswaldo Anibal","family":"Men\u00e9ndez","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda de Sistemas y Computaci\u00f3n, Universidad Cat\u00f3lica del Norte, Antofagasta 1249004, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7904-7981","authenticated-orcid":false,"given":"Miguel Attilio","family":"Torres-Torriti","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, School of Engineering, Faculty of Engineering, Pontificia Universidad Cat\u00f3lica de Chile, Santiago 7820436, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6372-7405","authenticated-orcid":false,"given":"Juan Pablo","family":"V\u00e1sconez","sequence":"additional","affiliation":[{"name":"Energy Transformation Center, Faculty of Engineering, Universidad Andr\u00e9s Bello, Santiago 7500000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2542-6545","authenticated-orcid":false,"given":"Tito","family":"Ar\u00e9valo-Ramirez","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering and Metallurgy, School of Engineering, Faculty of Engineering, Pontificia Universidad Cat\u00f3lica de Chile, Santiago 7820436, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7192-1681","authenticated-orcid":false,"given":"Alvaro Javier Prado","family":"Romo","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda de Sistemas y Computaci\u00f3n, Universidad Cat\u00f3lica del Norte, Antofagasta 1249004, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mascar\u00f3, M., Parra-Tsunekawa, I., Tampier, C., and Ruiz-del Solar, J. (2021). Topological Navigation and Localization in Tunnels\u2014Application to Autonomous Load-Haul-Dump Vehicles Operating in Underground Mines. Appl. Sci., 11.","DOI":"10.3390\/app11146547"},{"key":"ref_2","unstructured":"Cunningham, S. (2024, August 28). Proper Mining Conditions and How to Extend Your Mining Tire Life. Available online: https:\/\/maxamtire.com\/proper-mining-conditions-and-how-to-extend-your-mining-tire-life\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104451","DOI":"10.1016\/j.conengprac.2020.104451","article-title":"Tube-based nonlinear model predictive control for autonomous skid-steer mobile robots with tire\u2013terrain interactions","volume":"101","author":"Prado","year":"2020","journal-title":"Control Eng. Pract."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Urvina, R.P., Guevara, C.L., V\u00e1sconez, J.P., and Prado, A.J. (2024). An Integrated Route and Path Planning Strategy for Skid\u2013Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints. Agriculture, 14.","DOI":"10.20944\/preprints202406.0326.v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8045","DOI":"10.1109\/LRA.2021.3102328","article-title":"Distributed Tube-Based Nonlinear MPC for Motion Control of Skid-Steer Robots with Terra-Mechanical Constraints","volume":"6","author":"Prado","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Aro, K., Urvina, R., Deniz, N.N., Menendez, O., Iqbal, J., and Prado, A. (2023, January 25\u201327). A Nonlinear Model Predictive Controller for Trajectory Planning of Skid-Steer Mobile Robots in Agricultural Environments. Proceedings of the 2023 IEEE Conference on AgriFood Electronics (CAFE), Torino, Italy.","DOI":"10.1109\/CAFE58535.2023.10291643"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102105","DOI":"10.1016\/j.rineng.2024.102105","article-title":"Trajectory tracking for non-holonomic mobile robots: A comparison of sliding mode control approaches","volume":"22","author":"Medina","year":"2024","journal-title":"Results Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Prado, A.J., Herrera, M., Dominguez, X., Torres, J., and Camacho, O. (2022). Integral Windup Resetting Enhancement for Sliding Mode Control of Chemical Processes with Longtime Delay. Electronics, 11.","DOI":"10.3390\/electronics11244220"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4909","DOI":"10.1109\/TITS.2021.3054625","article-title":"Deep Reinforcement Learning for Autonomous Driving: A Survey","volume":"23","author":"Kiran","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Srikonda, S., Norris, W.R., Nottage, D., and Soylemezoglu, A. (2022). Deep Reinforcement Learning for Autonomous Dynamic Skid Steer Vehicle Trajectory Tracking. Robotics, 11.","DOI":"10.3390\/robotics11050095"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"012069","DOI":"10.1088\/1742-6596\/2303\/1\/012069","article-title":"Trajectory tracking control of wheeled mobile robot based on improved LSTM-DDPG algorithm","volume":"2303","author":"Gou","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wijayathunga, L., Rassau, A., and Chai, D. (2023). Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review. Appl. Sci., 13.","DOI":"10.20944\/preprints202304.0373.v1"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, S., and Wang, W. (March, January 28). Tracking Control for Mobile Robot Based on Deep Reinforcement Learning. Proceedings of the 2019 2nd International Conference on Intelligent Autonomous Systems (ICoIAS), Singapore.","DOI":"10.1109\/ICoIAS.2019.00034"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Batti, H., Jabeur, C.B., and Seddik, H. (2019, January 2\u20134). Fuzzy Logic Controller for Autonomous Mobile Robot Navigation. Proceedings of the 2019 International Conference on Control, Automation and Diagnosis (ICCAD), Grenoble, France.","DOI":"10.1109\/ICCAD46983.2019.9037922"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"De Paula, M., and Acosta, G.G. (2015, January 19\u201322). Trajectory tracking algorithm for autonomous vehicles using adaptive reinforcement learning. Proceedings of the OCEANS 2015\u2014MTS\/IEEE Washington, Washington, DC, USA.","DOI":"10.23919\/OCEANS.2015.7401861"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, N., Li, X., Zhang, K., Wang, J., and Xie, D. (2024). A Survey on Path Planning for Autonomous Ground Vehicles in Unstructured Environments. Machines, 12.","DOI":"10.3390\/machines12010031"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bouhamed, O., Ghazzai, H., Besbes, H., and Massoud, Y. (2020, January 12\u201314). Autonomous UAV Navigation: A DDPG-Based Deep Reinforcement Learning Approach. Proceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain.","DOI":"10.1109\/ISCAS45731.2020.9181245"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"29064","DOI":"10.1109\/ACCESS.2020.2971780","article-title":"Path Planning for UAV Ground Target Tracking via Deep Reinforcement Learning","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113667","DOI":"10.1016\/j.oceaneng.2023.113667","article-title":"DDPG based LADRC trajectory tracking control for underactuated unmanned ship under environmental disturbances","volume":"271","author":"Zheng","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"012030","DOI":"10.1088\/1742-6596\/2113\/1\/012030","article-title":"The application of Deep Reinforcement Learning in Coordinated Control of Nuclear Reactors","volume":"2113","author":"Li","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_21","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing Atari with Deep Reinforcement Learning. arXiv."},{"key":"ref_22","first-page":"14028","article-title":"Perceptual Interaction-Based Path Tracking Control of Autonomous Vehicles Under DoS Attacks: A Reinforcement Learning Approach","volume":"72","author":"Xu","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111974","DOI":"10.1016\/j.knosys.2024.111974","article-title":"Reinforcement learning-driven dynamic obstacle avoidance for mobile robot trajectory tracking","volume":"297","author":"Xiao","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hassan, I.A., Ragheb, H., Sharaf, A.M., and Attia, T. (2024, January 21\u201323). Reinforcement Learning for Precision Navigation: DDQN-Based Trajectory Tracking in Unmanned Ground Vehicles. Proceedings of the 2024 14th International Conference on Electrical Engineering (ICEENG), Cairo, Egypt.","DOI":"10.1109\/ICEENG58856.2024.10566281"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105051","DOI":"10.1016\/j.trc.2025.105051","article-title":"Coordinating ride-pooling with public transit using Reward-Guided Conservative Q-Learning: An offline training and online fine-tuning reinforcement learning framework","volume":"174","author":"Hu","year":"2025","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7546","DOI":"10.1109\/JIOT.2020.3038554","article-title":"A Bayesian Q-Learning Game for Dependable Task Offloading Against DDoS Attacks in Sensor Edge Cloud","volume":"8","author":"Liu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Du, Y., Ma, C., Liu, Y., Lin, R., Dong, H., Wang, J., and Yang, Y. (2022, January 23\u201327). Scalable Model-based Policy Optimization for Decentralized Networked Systems. Proceedings of the 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan.","DOI":"10.1109\/IROS47612.2022.9982253"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, W., Cui, Y., Li, H., and Wu, X. (2024). Practical Probabilistic Model-Based Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315.","DOI":"10.1109\/TNNLS.2024.3474169"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nowakowski, M. (2024, January 4\u20136). Operational Environment Impact on Sensor Capabilities in Special Purpose Unmanned Ground Vehicles. Proceedings of the 2024 21st International Conference on Mechatronics\u2014Mechatronika (ME), Brno, Czech Republic.","DOI":"10.1109\/ME61309.2024.10789716"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1109\/LRA.2019.2895892","article-title":"Rover-IRL: Inverse Reinforcement Learning with Soft Value Iteration Networks for Planetary Rover Path Planning","volume":"4","author":"Pflueger","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Guastella, D.C., and Muscato, G. (2021). Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review. Sensors, 21.","DOI":"10.3390\/s21010073"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, K., Niroui, F., Ficocelli, M., and Nejat, G. (2018, January 6\u20138). Robot Navigation of Environments with Unknown Rough Terrain Using deep Reinforcement Learning. Proceedings of the 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Philadelphia, PA, USA.","DOI":"10.1109\/SSRR.2018.8468643"},{"key":"ref_33","unstructured":"Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2019). Continuous control with deep reinforcement learning. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, S., Sun, C., Feng, Z., and Hu, G. (2019, January 18\u201320). Trajectory-Tracking Control of Robotic Systems via Deep Reinforcement Learning. Proceedings of the 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Bangkok, Thailand.","DOI":"10.1109\/CIS-RAM47153.2019.9095802"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Feh\u00e9r, \u00c1., Aradi, S., B\u00e9csi, T., G\u00e1sp\u00e1r, P., and Szalay, Z. (2020, January 12\u201315). Proving Ground Test of a DDPG-based Vehicle Trajectory Planner. Proceedings of the 2020 European Control Conference (ECC), St. Petersburg, Russia.","DOI":"10.23919\/ECC51009.2020.9143675"},{"key":"ref_36","first-page":"101950","article-title":"Trajectory planning and tracking control in autonomous driving system: Leveraging machine learning and advanced control algorithms","volume":"64","author":"Rahman","year":"2025","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_37","unstructured":"Fujimoto, S., van Hoof, H., and Meger, D. (2018). Addressing Function Approximation Error in Actor-Critic Methods. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5353","DOI":"10.1109\/LRA.2023.3295252","article-title":"High-Speed Autonomous Racing Using Trajectory-Aided Deep Reinforcement Learning","volume":"8","author":"Evans","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_39","unstructured":"Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., and Abbeel, P. (2019). Soft Actor-Critic Algorithms and Applications. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1109\/LRA.2020.2967299","article-title":"High-Speed Autonomous Drifting with Deep Reinforcement Learning","volume":"5","author":"Cai","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.cogr.2024.08.002","article-title":"Mobile robot path planning using deep deterministic policy gradient with differential gaming (DDPG-DG) exploration","volume":"4","author":"Deshpande","year":"2024","journal-title":"Cogn. Robot."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102254","DOI":"10.1016\/j.jksuci.2024.102254","article-title":"Deep reinforcement learning-based local path planning in dynamic environments for mobile robot","volume":"36","author":"Tao","year":"2024","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1016\/j.isatra.2024.10.026","article-title":"Heuristic dense reward shaping for learning-based map-free navigation of industrial automatic mobile robots","volume":"156","author":"Wang","year":"2025","journal-title":"ISA Trans."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e32167","DOI":"10.1016\/j.heliyon.2024.e32167","article-title":"Path planning of mobile robot based on improved TD3 algorithm in dynamic environment","volume":"10","author":"Li","year":"2024","journal-title":"Heliyon"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Frauenknecht, B., Ehlgen, T., and Trimpe, S. (2023, January 24\u201328). Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control. Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain.","DOI":"10.1109\/ITSC57777.2023.10422451"},{"key":"ref_46","first-page":"36","article-title":"A Comparison of PPO, TD3 and SAC Reinforcement Algorithms for Quadruped Walking Gait Generation","volume":"15","author":"Mock","year":"2023","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"ref_47","unstructured":"Sutton, R.S., and Barto, A.G. (1998). Reinforcement Learning: An Introduction, MIT Press. Adaptive Computation and Machine Learning."},{"key":"ref_48","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal Policy Optimization Algorithms. arXiv."},{"key":"ref_49","unstructured":"Schulman, J., Levine, S., Moritz, P., Jordan, M.I., and Abbeel, P. (2017). Trust Region Policy Optimization. arXiv."},{"key":"ref_50","unstructured":"Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. (2014, January 21\u201326). Deterministic policy gradient algorithms. Proceedings of the Proceedings of the 31st International Conference on International Conference on Machine Learning\u2014Volume 32, Beijing, China. ICML\u201914."},{"key":"ref_51","unstructured":"Heess, N., Hunt, J.J., Lillicrap, T.P., and Silver, D. (2015). Memory-based control with recurrent neural networks. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"De La Cruz, C., and Carelli, R. (2006, January 6\u201310). Dynamic Modeling and Centralized Formation Control of Mobile Robots. Proceedings of the IECON 2006\u201432nd Annual Conference on IEEE Industrial Electronics, Paris, France.","DOI":"10.1109\/IECON.2006.347299"},{"key":"ref_53","unstructured":"Ng, A.Y., Harada, D., and Russell, S.J. (1999, January 27\u201330). Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. Proceedings of the Sixteenth International Conference on Machine Learning, San Francisco, CA, USA. ICML \u201999."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1177\/0278364916679497","article-title":"Chilean underground mine dataset","volume":"36","author":"Leung","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_55","unstructured":"Codelco (2025, April 18). Divisi\u00f3n El Teniente|CODELCO. Available online: https:\/\/dataset.amtc.cl\/index.php\/overview\/."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The Whale Optimization Algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ashraf, N.M., Mostafa, R.R., Sakr, R.H., and Rashad, M.Z. (2021). Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0252754"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Alcayaga, J., Camacho, C., Dur\u00e1n, F., and Romo, A.P. (2024, January 15\u201318). On the assessment of reinforcement learning techniques for trayectory tracking of autonomous ground robots. Proceedings of the 2024 IEEE Eighth Ecuador Technical Chapters Meeting (ETCM), Cuenca, Ecuador.","DOI":"10.1109\/ETCM63562.2024.10746097"}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/6\/74\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:43:00Z","timestamp":1760031780000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/6\/74"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,29]]},"references-count":58,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["robotics14060074"],"URL":"https:\/\/doi.org\/10.3390\/robotics14060074","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,29]]}}}