reinforcement learning

Probability Logic Networks: Adaptive Learning Mechanisms

Probability Logic Networks (PLN) represent a powerful framework for learning complex patterns from data. Traditional implementations often rely on static network configurations, limiting their ability to adapt to evolving data and improving model generalization. This paper introduces a novel adaptive learning mechanism...

Title: Non-Linear Phase Transition Modeling with Adaptive Parameter Tuning

This paper presents a novel system for non-linear phase transition modeling, designed to automatically tune model parameters based on observed data. Traditional approaches often rely on fixed parameter sets, limiting predictive accuracy. Our system employs a learning mechanism to dynamically adjust model parameters, of...

Title: Dynamic Quantum Entanglement Distribution for Distributed Computing

Quantum entanglement is a fundamental resource for distributed quantum computing, enabling complex computations that are intractable for classical computers. However, establishing and maintaining entanglement across a network of nodes presents significant challenges due to decoherence and communication overhead. This p...

Dynamic Programming with Reinforcement Learning for Scheduling Optimization

This paper presents a novel approach to scheduling optimization that synergistically combines the strengths of dynamic programming (DP) and reinforcement learning (RL). Traditional DP methods struggle with complex scheduling scenarios due to their exponential computational complexity, particularly when dealing with int...

Adaptive Quantum State Collapse for Machine Learning Training

This paper investigates the application of adaptive quantum state collapse within the context of machine learning training. Traditional training methods often rely on static parameters, limiting the model's ability to adapt to complex data patterns. We propose a novel approach leveraging reinforcement learning to dynam...

Emergent Multi-Agent Temporal Dynamics (EMATD)

This paper introduces the Emergent Multi-Agent Temporal Dynamics (EMATD) framework, a novel approach to modeling temporal dynamics within complex systems. EMATD leverages reinforcement learning to train agents within a dynamically changing environment, resulting in the emergence of collective temporal patterns. The cor...

Power-fade-aware deep reinforcement learning for second-life battery energy management

Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework

Modelling Reinforcement Learning Scheduling Agents: Action spaces, reward designs, and expert demonstrations

PERENCANAAN PEMBELAJARAN KIMIA BERBASIS DIGITAL DALAM MEMPERKUAT MORAL GENERASI Z DI SMA MUHAMMADIYAH 13 JAKARTA

The inherent characteristics of Generation Z with digital devices trigger moral degradation and ethical decline due to unfiltered digital information flows. This condition requires educational institutions to design science learning strategies that integrate technological skills with noble character cultivation. This q...

Genetic Algorithms and Reinforcement Learning Approaches for Joint Placement, Power Control, and Activation of Flying Base Stations

AMIS-DynaFlow: a multi-agent deep reinforcement learning framework with quantum-inspired operators for real-time patient flow optimization in oncology centers