DSpace-CRIS

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Implementation of Deep Reinforcement Learning for Radio Telescope Control and Scheduling
(2025-12-01)
Puangragsa, Sarut
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Sahavisit, Tanawit
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Laon, Popphon
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Puangragsa, Utumporn
;
Phasukkit, Pattarapong
The proliferation of terrestrial and space-based communication systems introduces significant radio frequency interference (RFI), which severely compromises data acquisition for radio telescopes, necessitating robust and dynamic scheduling solutions. This study addresses this challenge by implementing a Deep Recurrent Reinforcement Learning (DRL) framework for the control and dynamic scheduling of the X-Y pedestal-mounted KMITL radio telescope, explicitly trained for RFI avoidance. The methodology involved developing a custom simulation environment with a domain-specific Convolutional Neural Network (CNN) feature extractor and a Long Short-Term Memory (LSTM) network to model temporal dynamics and long-horizon planning. Comparative evaluation demonstrated that the recurrent DRL agent achieved a mean effective survey coverage of 475 deg<sup>2</sup>/h, representing a 72.7% superiority over the non-recurrent baseline, and maintained exceptional stability with only 1.0% degradation in median coverage during real-world deployment. The DRL framework offers a highly reliable and adaptive solution for telescope scheduling that is capable of maintaining survey efficiency while proactively managing dynamic RFI sources.
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Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
(2024-09-01)
Meepokgit, Teerapun
;
Wisayataksin, Sumek
Traffic lights are the most commonly used tool to manage urban traffic to reduce congestion and accidents. However, the poor management of traffic lights can result in further problems. Consequently, many studies on traffic light control have been conducted using deep reinforcement learning in the past few years. In this study, we propose a traffic light control method in which a Deep Q-network with fuzzy logic is used to reduce waiting time while enhancing the efficiency of the method. Nevertheless, existing studies using the Deep Q-network may yield suboptimal results because of the reward function, leading to the system favoring straight vehicles, which results in left-turning vehicles waiting too long. Therefore, we modified the reward function to consider the waiting time in each lane. For the experiment, Simulation of Urban Mobility (SUMO) software version 1.18.0 was used for various environments and vehicle types. The results show that, when using the proposed method in a prototype environment, the average total waiting time could be reduced by 18.46% compared with the traffic light control method using a conventional Deep Q-network with fuzzy logic. Additionally, an ambulance prioritization system was implemented that significantly reduced the ambulance waiting time. In summary, the proposed method yielded better results in all environments.
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Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation
(2023-12-01)
Karunanayake, Nalan
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Moodleah, Samart
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Makhanov, Stanislav S.
A segmentation model of the ultrasound (US) images of breast tumors based on virtual agents trained using reinforcement learning (RL) is proposed. The agents, living in the edge map, are able to avoid false boundaries, connect broken parts, and finally, accurately delineate the contour of the tumor. The agents move similarly to robots navigating in the unknown environment with the goal of maximizing the rewards. The individual agent does not know the goal of the entire population. However, since the robots communicate, the model is able to understand the global information and fit the irregular boundaries of complicated objects. Combining the RL with a neural network makes it possible to automatically learn and select the local features. In particular, the agents handle the edge leaks and artifacts typical for the US images. The proposed model outperforms 13 state-of-the-art algorithms, including selected deep learning models and their modifications.
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A Novel Augmentative Backward Reward Function with Deep Reinforcement Learning for Autonomous UAV Navigation
(2022-01-01)
Chansuparp, Manit
;
Jitkajornwanich, Kulsawasd
The autonomous UAV (unmanned aerial vehicle) navigation has recently gained an increasing interest from both academic and industrial sectors due to its potential uses in various fields and especially, the need for social distancing during the pandemic. Many works have adopted a deep reinforcement learning (RL) method with experience replay called deep deterministic policy gradient (DDPG) to control the motion of UAV, and gain high accuracy results in static and simplified environments. However, they are still far from being ready for real world adoption in that the UAVs have to operate under complex and dynamic conditions. We also found that using only DDPG makes the learning process prone to oscillation and is inefficient for tasks having high dimensional action-state spaces. Furthermore, the goal reward mechanism in traditional reward functions brings a bias to the state, which resembles the one at the goal area and leads to erroneous action selection. To get closer to being ready for real world adoption, we proposed a novel method that enables UAVs to be capable of handling motion control in realistic environments. The first component of our proposed method is point cloud data (PCD) simplification with truncated icosahedron structure which converts enormous PCD into a few essential data points. In the second component of our method, we replace the traditional goal reward mechanism with a new mechanism called Augmentative Backward Reward (ABR) function to dispense the goal reward to transitions proportionately to its participation. By integrating simplified PCD and ABR, we achieved significantly better results when compared with using only the-state-of-the-art, TD3. In addition, we tested the proposed method with another navigation task, BipedalWalkerHardcore, a testbed for RL, and the result is still better and steadier than of TD3. These results indicate that the proposed method is robust.

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