reinforcement learning

SRTS: Semantic Representations for Trajectory Stitching in Offline Reinforcement Learning

Neuro-Topology Adaptive Dynamics

This paper introduces Neuro-Topology Adaptive Dynamics (NTAD), a novel approach to neural network design that leverages reinforcement learning to dynamically optimize the topology of a neural network. The core claim is that by adapting the network's connectivity structure in real-time, using algorithms like Proximal Po...

Title: Universal Chaos Theory with Adaptive Parameter Tuning

This paper explores a novel approach to understanding and manipulating universal chaos within complex systems, moving beyond static parameter settings. We propose a method based on Bayesian inference and reinforcement learning to dynamically optimize parameters of a chaotic system, fostering stable, high-dimensional st...

Dynamic Symbolic Computation via Temporal Logic and Reinforcement Learning

This paper presents a novel approach to dynamic symbolic computation by integrating temporal logic reasoning with reinforcement learning. The core idea is to enable a system to adaptively learn and optimize its symbolic representations through a feedback mechanism. We introduce an agent that utilizes temporal logic con...

Adaptive Topology for Quantum Circuit Design

Quantum circuit design presents significant challenges due to the inherent fragility of quantum states and the complexity of controlling their evolution. Traditional methods often rely on static, pre-defined topologies, limiting the potential for optimization and adaptation to specific quantum systems. This paper intro...

Dynamically Weighted Graph Convolutional Networks for Adaptive Feature Learning

This paper proposes a novel approach to Graph Convolutional Networks (GCNs) that addresses the limitations of static weight assignment in traditional GCNs. We introduce Dynamically Weighted Graph Convolutional Networks (DW-GCNs) which adaptively learn connection weights based on the evolving dynamics of the graph struc...

Dynamic Neural Topography Learning (NTL)

This paper introduces Dynamic Neural Topography Learning (NTL), a novel approach to neural network architecture design that leverages the dynamic relationship between physical sensor inputs and the hierarchical structure of a neural network. The core claim is that by dynamically adapting the network's topology based on...

Distributed Edge Computing Resource Scheduling and Optimization

This paper investigates the challenges and proposes a novel approach to distributed edge computing resource scheduling and optimization. The core claim is to design an efficient algorithm that dynamically allocates resources based on task requirements and edge computing node capabilities, ultimately enhancing computati...

Dynamic Bayesian Optimization with Reinforcement Learning for Hyperparameter Tuning

Hyperparameter tuning is a critical, yet computationally expensive, component of modern machine learning workflows. Traditional Bayesian Optimization (BO) methods, while effective in many scenarios, often struggle when dealing with high-dimensional hyperparameter spaces, leading to slow convergence and suboptimal perfo...

Adaptive Stochastic Resonance (ASR)

Adaptive Stochastic Resonance (ASR) is a novel signal processing technique that dynamically adjusts the stochastic parameters of a signal based on the observed response, resulting in a significant improvement in the signal-to-noise ratio. This paper explores the core mechanism behind ASR, detailing the reinforcement le...

A data-driven framework for state-space optimization in reinforcement learning control of electric vehicle thermal management

Neurogenesis Reinforcement Learning Language Model

We propose the Neurogenesis Reinforcement Learning Language Model (NRLLM), a research framework in which stable linguistic mastery can trigger endogenous growth of a language agent trained through reinforcement learning. Godrick is the proposed experimental realization. Its defining hypothesis is that an exactly-one-ne...