• Mohamad Hafiz Abu Bakar Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia https://orcid.org/0009-0005-2572-6718
  • Abu Ubaidah Shamsudin Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia https://orcid.org/0000-0002-7917-5967
  • Zubair Adil Soomro Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia https://orcid.org/0000-0003-0297-3909
  • Satoshi Tadokoro Tohoku University, 2 Chome-1-1 Katahira, Aoba Ward, Sendai, Miyagi 980-8577, Japan
  • C. J. Salaan Department of Electrical Engineering and Technology, MSU-Iligan Institute of Technology, Andres Bonifacio Ave, Iligan City, 9200 Lanao del Norte, Philippines




Soft Actor Critic Deep Reinforcement Learning (SAC DRL), Deep Reinforcement Learning, Mobile robot navigation, Reward function, Sparse reward, Shaping reward


Nowadays, the advancement in autonomous robots is the latest influenced by the development of a world surrounded by new technologies. Deep Reinforcement Learning (DRL) allows systems to operate automatically, so the robot will learn the next movement based on the interaction with the environment. Moreover, since robots require continuous action, Soft Actor Critic Deep Reinforcement Learning (SAC DRL) is considered the latest DRL approach solution. SAC is used because its ability to control continuous action to produce more accurate movements. SAC fundamental is robust against unpredictability, but some weaknesses have been identified, particularly in the exploration process for accuracy learning with faster maturity. To address this issue, the study identified a solution using a reward function appropriate for the system to guide in the learning process. This research proposes several types of reward functions based on sparse and shaping reward in SAC method to investigate the effectiveness of mobile robot learning. Finally, the experiment shows that using fusion sparse and shaping rewards in the SAC DRL successfully navigates to the target position and can also increase accuracy based on the average error result of 4.99%.


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How to Cite

Abu Bakar, M. H., Shamsudin, A. U., Soomro, Z. A., Tadokoro, S., & Salaan, C. J. (2024). FUSION SPARSE AND SHAPING REWARD FUNCTION IN SOFT ACTOR-CRITIC DEEP REINFORCEMENT LEARNING FOR MOBILE ROBOT NAVIGATION. Jurnal Teknologi, 86(2), 37–49. https://doi.org/10.11113/jurnalteknologi.v86.20147



Science and Engineering