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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Atma Inovasia
Indra Karnadi
Universitas Kristen Krida Wacana
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Social Sciences Other

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Imitation Learning to Accelerate Training Process of Multi-Agent Reinforcement Learning in 2v2 Pong Game Marvin Yonathan Hadiyanto; Budi Harsono; Indra Karnadi; Ivan Tanra
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2564

Abstract

Training multi-agent reinforcement learning (MARL) systems often requires a significant amount of time due to sample inefficiency, particularly when agents must perform extensive exploration in complex environments and coordinate among multiple entities. This study proposes the use of imitation learning to accelerate the MARL training process in a 2v2 pong game by leveraging demonstrations from a 1v1 pong game to shape the initial policy without undergoing inefficient exploration procedures. We employ a deep Q-network (DQN) framework with centralized training and decentralized execution (CTDE) to compare the performance of pretrained and untrained agents in the 2v2 pong environment. Experimental results show that learning from demonstrations in the 1v1 setting improves reward accumulation and game scores of pretrained agents in the 2v2 pong game. The performance improvement peaks at 700 demonstration learning steps and diminishes at larger learning steps due to excessive memorization of the demonstration gameplay. Furthermore, comparative experiments demonstrate that imitation learning with 700 learning steps achieves learning efficiency improvements of approximately 300% and 571% compared to the zonation method and standard reinforcement learning pretraining, respectively. These results indicate that imitation learning from demonstrations can effectively reduce the prolonged training process in MARL, offering a viable solution, particularly when data collection, computational resources, and training time are severely constrained.
Pelatihan Robotika Tempat Sampah Pintar dan Miniatur Selektor Otomatis untuk Guru Elektronika SMP BPK PENABUR Kevin Sutanto; Marvin Yonathan Hadiyanto; Johansah Liman; Eddy Wijanto; Ivan Tanra; Indra Karnadi; Richie Estrada; Budi Harsono
Jurnal Atma Inovasia Vol. 6 No. 3 (2026)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jai.v6i3.14056

Abstract

The advancement of robotics technology enables project based instructional media to be integrated into secondary education as a means to enhance technological literacy and problem solving skills among students. This community engagement program aimed to enhance the competencies of electronics subject teachers at SMP BPK PENABUR Jakarta through training on the design and implementation of basic Arduino based robotic systems. The training was conducted onsite using a hands-on approach and featured two applied projects: an ultrasonic based smart trash bin and a conveyor based automatic object selector. Participants gained practical experience in sensor introduction, simple block programming, and system functionality testing. Evaluation results indicated that the training was positively received and considered relevant to the school’s instructional needs in integrating technology into learning modules. This program contributed to improving teachers readiness to implement applied robotics learning and to strengthening STEM literacy foundations at the secondary education level.
Co-Authors BUDI HARSONO Eddy Wijanto Johansah Liman Marvin Yonathan Hadiyanto Richie Estrada Sutanto, Kevin Tanra, Ivan