Show simple item record

dc.contributor.authorFikry, Muhammad
dc.contributor.authorAdek, Rizal Tjut
dc.contributor.authorHartanto, Subhan
dc.contributor.authorTaufiqurrahman
dc.contributor.authorRinawati, Dyah Ika
dc.date.accessioned2024-01-04T07:36:59Z
dc.date.available2024-01-04T07:36:59Z
dc.date.issued2022-04-01
dc.identifier.citationFikry, Muhammad, Rizal Tjut Adek, Zulfhazli Zulfhazli, Subhan Hartanto, Taufiqurrahman Taufiqurrahman, dan Dyah Ika Rinawati. 2022. “Analysis of Model-Free Reinforcement Learning Algorithm for Target Tracking.” Journal of Computer Engineering, Electronics and Information Technology 1 (1): 01–10. https://doi.org/10.17509/coelite.v1i1.43795.en_US
dc.identifier.issn2829-4149
dc.identifier.urihttps://ejournal.upi.edu/index.php/COELITE/article/view/43795
dc.description.abstractTarget tracking is a process that can find points in different domains. In tracking, some places contain prizes (positive or negative values) that the agent does not know at first. Therefore, the agent, which is a system, must learn to get the maximum value with various learning rates. Reinforcement learning is a machine learning technique in which agents learn through interaction with the environment using reward functions and probabilistic dynamics to allow agents to explore and learn about the environment through various iterations. Thus, for each action taken, the agent receives a reward from the environment, which determines positive or negative behavior. The agent's goal is to maximize the total reward received during the interaction. In this case, the agent will study three different modules, namely sidewalk, obstacle, and product, using the Q-learning algorithm. Each module will be training with various learning rates and rewards. Q-learning can work effectively with the highest final reward at a learning rate of 0.8 for 500 rounds with an epsilon of 0.9.en_US
dc.language.isootheren_US
dc.publisherJournal of Computer Engineering, Electronics and Information Technology (COELITE)en_US
dc.relation.ispartofseries1;1
dc.subjectAlgorithmen_US
dc.subjectMachine Learningen_US
dc.subjectProbabilisticen_US
dc.subjectQ-Learningen_US
dc.subjectReinforcement Learningen_US
dc.subjectTarget Trackingen_US
dc.titleAnalysis of Model-Free Reinforcement Learning Algorithm for Target Trackingen_US
dc.typeArticleen_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record