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9 articles for “Reward functions”
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Modified Binary Firefly Algorithm (MBFA) for Optimal Allocation of Spectrum in Cognitive Radio Systems
Abstract: Spectrum bands are nature’s communication highways which are highly congested now-adays. To overcome this problem, dynamic allocation of spectrum is under consideration over the current fixed spectrum allotment policy. In dynamic allotment, various optimisation methods can be applied for achieving the best possible spectrum allotment during handover so that the better quality of service can be given to cognitive users with the simultaneous improvement in spectrum utilisation. This study focuses …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 3, Issue 2, 2016 · pp. 42–47 Read article
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Randomized Latent Vectors for Enhanced Reinforcement Learning Exploration
Abstract: This paper investigates Random Latent Exploration (RLE), a novel reinforcement learning technique that enhances exploration using randomized latent vector conditioning. I evaluate RLE’s performance across various environments, including discrete control tasks (FourRoom), continuous control (IsaacLab), and complex visual domains (Atari games). The core approach augments traditional reward functions with intrinsic rewards, calculated as the dot product between state features and periodically resampled latent vectors. The policy and value networks are …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 19–25 Read article
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Reward-Aligned Reinforcement Learning from Human Feedback for Emotion-Sensitive Large Language Model Therapists: Balancing Empathetic Engagement, Boundary Safety, and Clinical Accountability
Abstract: The deployment of large language models (LLMs) in mental health therapy contexts introduces a critical alignment challenge: these systems must simultaneously cultivate genuine empathic rapport, observe clinically grounded safety boundaries, and remain auditable under institutional accountability frameworks. Existing reinforcement learning from human feedback (RLHF) pipelines optimize for a scalar reward signal that is demonstrably insufficient for the multi-objective, temporally extended nature of therapeutic conversation. This paper presents RA-RLHF-T (Reward-Aligned RLHF …
Published in International Journal of Tropical Medicines · Vol. 3, Issue 2, 2026 Read article
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DC Motor Control using Deep Reinforcement Learning for Enhanced Robustness and Precision
Abstract: DC motors remain the workhorse of industrial automation and mobile robotics, but achieving simultaneous high-speed transient response and negligible steady-state error under variable load conditions continues to challenge classical Proportional-Integral-Derivative (PID) controllers. These model-dependent systems often require extensive tuning and struggle to maintain optimal performance when confronted with parametric uncertainties, non-linear friction, or sudden voltage fluctuations. This study presents a novel, model-free control paradigm utilizing Deep Reinforcement Learning (DRL)—specifically, a …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 3, Issue 2, 2025 · pp. 22–29 Read article
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Machine Learning Based Optimization of Polymer Structure Property Relationships in Composite Material Systems
Abstract: In modern engineering applications, polymer-based composite materials have garnered a lot of attention because of their lightweight nature, high strength-to-weight ratio, and changing physical features. In order to maximize the relationships between polymer structure and properties in composite materials, this study suggests a strategy based on reinforcement learning (RL). The research utilized the Polymer Composite Properties Dataset, which contains 12,700 records associated with polymer matrices, reinforcement fillers, interfacial bonding characteristics, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 242–255 Read article
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Comparative Study of Reinforcement Learning Algorithms on Traffic Light Control System
Abstract: With the changing times, the need of upgraded and efficient state-of-the-art traffic light control system is truly required. How well do various state-of-the-art algorithms handle complex real-life situations, more technically speaking, how well the reward function operates and minimizes the waiting time, is the prime question of the hour. Here we have successfully employed three reinforcement learning agents-REINFORCE, D3QN and DDPG each of which can independently handle large volumes of …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 8, Issue 3, 2021 · pp. 33–45 Read article
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MicroRNAs in Animal Behavior: Regulation of Memory and Decision-Making in Mammals
Abstract: MicroRNAs are small, non-coding RNA molecules that play a crucial role in post-transcriptional regulation of gene expression. In recent years, their significance has extended beyond cellular development and disease regulation, as emerging evidence suggests they are critical mediators in complex behavioral processes, including learning, memory, and decision-making. This article explores the role of miRNAs in regulating neural plasticity and cognitive functions in mammals, emphasizing their involvement in memory formation and …
Published in Research and Reviews : Journal of Veterinary Science and Technology · Vol. 13, Issue 3, 2024 · pp. 21–25 Read article
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Toxicological Examination of Dopamine's Involvement in Substance Abuse: An Overview
Abstract: Dopamine, classified as a catecholamine, is an essential organic compound that plays a crucial role in the brain's reward system and regulatory processes. It is instrumental in reinforcing pleasurable activities through repeated engagement. Acting as a neurotransmitter, dopamine is released in various brain regions, including the striatum and accumbens, and is absorbed by neurons through receptors.This review aims to comprehensively outline the mechanisms of dopamine release and its pivotal role …
Published in Research and Reviews: A Journal of Toxicology · Vol. 13, Issue 3, 2023 · pp. 22–25 Read article
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India's Medical Education through E-learning
Abstract: E-learning or learning and teaching that is supported and facilitated by the use of technology, is currently used extensively in all areas of education, including medical education. The purpose of this narrative review is to introduce the idea of e-learning and to discuss its value and application in Indian medical education. The majority of students and faculty, according to experience, are in favour of integrating e-learning alongside traditional learning because …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 12, Issue 2, 2022 · pp. 31–34 Read article