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The demand for transportation has increased significantly in recent decades in line with the increasing demand for passenger and freight mobility, especially in urban areas. One of the most negative impacts is the increasing level of traffic congestion. A possible short-term solution to solve this problem is to utilize a traffic control system. However, most traffic control systems still use classical control algorithms with the green phase sequence determined, based on a specific strategy. Studies have proven that this approach does not provide the expected congestion solution. In this paper, an adaptive traffic controller was developed that uses a reinforcement learning algorithm called deep Q-network (DQN). Since the DQN performance is determined by reward selection, an exponential reward function, based on the macroscopic fundamental diagram (MFD) of the distribution of vehicle density at intersections was considered. The action taken by the DQN is determining traffic phases, based on various rewards, ranging from pressure to adaptive loading of pressure and queue length. The reinforcement learning algorithm was then applied to the SUMO traffic simulation software to assess the effectiveness of the proposed strategy. The DQN-based control algorithm with the adaptive reward mechanism achieved the best performance with a vehicle throughput of 56,384 vehicles, followed by the classical and conventional control methods, such as Webster (50,366 vehicles), max-pressure (50,541 vehicles) and uniform (46,241 vehicles) traffic control. The significant increase in vehicle throughput achieved by the adaptive DQN-based control algorithm with an exponential reward mechanism means that the proposed traffic control could increase the area productivity, implying that the intersections could accommodate more vehicles so that the possibility of congestion was reduced. The algorithm performed remarkably in preventing congestion in a traffic network model of Central Jakarta as one of the world’s most congested cities. This result indicates that traffic control design using MFD as a performance measure can be a successful future direction in the development of reinforcement learning for traffic control systems.

Viskometer bola jatuh merupakan alat ukur viskositas dengan cara mengukur waktu yang dibutuhkan sebuah bola untuk melewati cairan dengan jarak tertentu berdasarkan prinsip Hukum Stokes dan Hukum Newton. Perhitungan secara manual pada waktu tempuh bola dan nilai viskositas cairan menyebabkan kesalahan paralaks. Oleh sebab itu, pada penelitian ini dibuat prototipe viskometer bola jatuh yang dapat mengukur waktu tempuh bola dan mengolahnya untuk mendapatkan nilai koefisien viskositas secara otomatis. Prototipe pada penelitian ini menggunakan dua buah closedcircuit magnetic sensor untuk mendeteksi waktu tempuh bola magnet saat dijatuhkan pada cairan uji. Waktu tempuh diolah menjadi kecepatan bola magnet dan nilai koefisien viskositas cairan (dPa.s) lalu ditampilkan di komputer menggunakan perangkat arduino dan LabView 8.5. Prototipe dapat mengukur waktu tempuh bola secara otomatis dan menampilkan nilai koefisien viskositas pada komputer. Hasil pengukuran nilai viskositas menggunakan prototipe sebagai berikut minyak goreng 5.46 dPa.s ; SAE 40 24.67 dPa.s dan silicone oil 22.97 dPa.s. Nilai tersebut jauh dari nilai viskositas referensi disebabkan faktor konstanta yang digunakan pada persamaan viskometer bola jatuh tidak dapat disesuaikan seperti teori yang ada karena syarat ukuran gelas ukur sebagai wadah cairan pada prototipe dibandingkan dengan ukuran bola tidak terpenuhi. Sehingga ada faktor lain yang perlu dipertimbangkan agar diperoleh hasil pengukuran nilai viskositas cairan yang mendekati nilai sebenarnya.

Purpose The purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research. Design/methodology/approach First, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions. Findings The discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment. Originality/value This paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.

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