reinforcement learning

tagi9332/RPOD_RL: v1.0.0 - Thesis release

Release of reinforcement learning software package for MS thesis defense including model used in monte carlo analyses.

MEMBANGUN LINGKUNGAN PESANTREN BEBAS BULLYING PERSPEKTIF TEORI KOGNITIF SOSIAL ALBERT BANDURA DI PONDOK PESANTREN PUTRI HASAN JUFRI

ABSTRACT The dynamics of bullying within Islamic boarding schools are intricately intertwined with patterns of social relationships and student character development, thereby demanding sustainable guidance patterns. This study specifically aims to describe and interpret the efforts of asatiz in building a bullying-free...

Neuromorphic control of a simulated shape-memory-alloy-driven multi-legged robot using a spiking neural network

Abstract We present spiking-neural-network (SNN) reinforcement learning for autonomous locomotion control of a starfish-inspired multi-legged soft robot driven by shape memory alloy (SMA) actuators. Such robots are usually driven open loop by pre-designed periodic signals, because coordinated motion is hard to hand-des...

Autonomous Kill-Switch Architectures for Frontier AGI: Combining Quantum Entanglement Interlocks with Hardware-Level Circuit Breakers and Fiduciary Caremark Governance

Description / Abstract: Contemporary artificial general intelligence (AGI) safety frameworks rely predominantly on upper-layer software abstractions—reinforcement learning from human feedback (RLHF), constitutional prompt wrappers, and automated runtime guardrails. Because deep neural networks are uninterpretable non-l...

Natural and engineered safe reinforcement learning

Abstract Survival depends not only on maximising rewards but on minimising harm. While Reinforcement Learning (RL) provides a normative framework for adaptive decision-making, standard algorithms often fail to account for the catastrophic costs of errors during learning, in the same way as animals do. In this review, w...

A CLOUD-BASED GRID COLLAPSE PREDICTION AND DISASTER RECOVERY MANAGEMENT SYSTEM: A CASE OF THE NIGERIAN POWER SECTOR

Nigeria’s power grid experiences recurring partial and total collapses that cause widespread electricity outages and socioeconomic disruption. This study proposes a cloud-based system that combines grid-collapse prediction with coordinated disaster-recovery management. The predictive component uses a hybrid Convolution...

Hierarchical reinforcement learning using a world model for multi-objective optimization of micro-milling

Social Prediction Error Gating for Adaptive Social Learning: A Computational Model of Individual and Social Information Integration

This paper presents Social Prediction Error Gating (SPEG), a computational reinforcement-learning model designed to combine individual experience with social information based on estimated partner reliability and uncertainty. The proposed model is evaluated through simulations of patch-selection foraging in stable and...

Integrated Geometry and Adaptive PTO Optimisation of Hybrid Wind–Wave Energy Systems Using Reinforcement Learning-Assisted Co-Evolution

Hybrid wind–wave energy systems (HWESs) require coordinated geometry and control design to balance long-term energy capture and floating-platform response under variable offshore conditions. This study develops a constrained multi-objective framework that jointly optimises fixed wave energy converter (WEC) geometry and...

Artificial Intelligence and Data Science Applications in Anaerobic Digestion: Monitoring and Optimization Strategies

Abstract Anaerobic digestion (AD) is a significant biological process that transforms organic waste into renewable biogas and nutrient-dense digestate. This can be returned as fertilizer, which promotes waste management and renewable energy production. However, typical monitoring relies on offline laboratory measuremen...

A web development method for transmission line O&M components based on multi-source data sensing and deep reinforcement learning algorithms

Reinforcement Learning Fine‐tuning

This chapter extends the fine-tuning pipeline by introducing reinforcement learning fine-tuning, which addresses limitations of supervised fine-tuning when optimizing for subjective qualities like helpfulness, safety, or user preference alignment. The chapter presents the complete supervised fine-tuning followed by rei...