Smart Computing and Systems Engineering - 2025 (SCSE 2025)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/30037

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Subject: search.filters.subject.Reinforcement learning

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    Gait pattern analysis for a weight carrying hexapod ant robot using reinforcement learning
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Karunarathna, J. A. T. D. B.; Mohamed Saki, W.; Kanesalingam, S.; Prasanga, D. K.; Weerasinghe, W. A. B. G. H. B. P.; Abeykoon, A. M. H. S.; Ruwanthika, R. M. M.
    Robot hexapods are coming into focus in robotics fields due to their stable base and versatility in handling different terrains. The current study aims at identifying gait patterns to enhance efficiency and stability under dynamic payload conditions. It investigated six payload conditions: no payload, central payload, and four asymmetric payloads, which were front-left and back-left and front-right and back-right. To enable the dynamic modification of gait patterns, Proximal Policy Optimization (PPO), which is a type of reinforcement learning, was used in order to foster efficient and stable forward propulsion. To train and simulate a robot, Brax, an open-source physics simulation environment, was used under different payload conditions. It was shown that gait adaptation to loading distribution is achievable, while bilateral loading causes energy expenditures to grow. The research on hexapod movement is useful in the advancement of the field of bio-inspired robotics; it provides ideas for increasing hexapod mobility with unequal weight loadings and also helps to further extend hexapod robots’ usability.
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    Enhancing network intrusion detection with stacked deep and reinforcement learning models
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Kalpani, N.; Rodrigo, N.; Seneviratne, D.; Ariyadasa, S.; Senanayake, J.
    This study investigates the effectiveness of Ensemble Learning (EL) techniques by integrating reproducible Deep Learning (DL) and Reinforcement Learning (RL) models to enhance network intrusion detection. Through a systematic review of the literature, the most effective DL and RL models from 2020 to 2024 were identified based on their F1 scores and reproducibility, focusing on recent advancements in network intrusion detection. A structured normalisation and evaluation process allowed for an objective comparison of model performances. The best performing DL and RL models were subsequently integrated using a stacking ensemble technique, chosen for its ability to combine the complementary strengths of the DL and RL models. Experimental validation in a benchmark dataset confirmed the high accuracy and robust detection capabilities of the model, outperforming the individual DL and RL models to detect network intrusions in multiple classes. This research demonstrates the potential of ensemble methods for advancing Intrusion Detection Systems (IDSs), offering a scalable and effective solution for dynamic cybersecurity environments.