reinforcement learning

基于深度强化学习的硬件资源动态分配

This paper investigates the application of Deep Reinforcement Learning (DRL) for dynamic hardware resource allocation in complex systems. Traditional resource allocation methods often rely on static rules, which struggle to adapt to fluctuating workloads and system states. This research proposes a DRL-based approach wh...

Title: Adaptive Constraint-Based Fractal Generation

This paper introduces an adaptive constraint-based fractal generation system designed to produce visually compelling and optimized fractal images. The core innovation lies in a reinforcement learning algorithm that dynamically adjusts fractal parameters based on feedback from the input data, allowing for continuous opt...

Dynamic Quantum Circuit Compilation via Reinforcement Learning

Quantum circuit compilation is a crucial step in realizing quantum algorithms on real quantum hardware. Traditionally, compilation methods are largely static, relying on pre-defined mappings and optimizations that fail to account for the inherent noise and variability present in quantum devices. This paper proposes a n...

Dynamic Probabilistic Graphical Model Learning and Reinforcement Learning Fusion

This paper presents a novel framework for intelligent learning and decision-making in complex environments by fusing probabilistic graphical model learning with reinforcement learning. The core idea is to construct a dynamic system capable of learning both the structure and parameters of probabilistic graphical models,...

Reinforcement Learning for Hardware-Aware Neural Network Design

The design of neural networks for deployment on specialized hardware is a significant bottleneck in the widespread adoption of deep learning. Traditional methods often rely on manual tuning or computationally expensive search algorithms, failing to effectively account for the unique constraints and opportunities presen...

Adaptive Bayesian Networks for Online Anomaly Detection

This paper proposes a novel approach to online anomaly detection utilizing adaptive Bayesian Networks (ABNs). Traditional Bayesian Networks (BNs) often fail to effectively handle the dynamic nature of real-world data streams, leading to inaccurate anomaly detection. Our approach overcomes this limitation by dynamically...

Dynamic Topology Adaptive Neuromorphic Networks

This research proposes a novel approach to neural network design centered around dynamic topology adaptation. The core concept leverages the principles of biological neural networks, specifically incorporating dynamic synaptic plasticity and connection modulation, to create networks capable of real-time adaptation to c...

Title: Dynamical Topology Optimization for Complexity

Dynamical Topology Optimization (DTO) provides a framework for optimizing complex network topologies by iteratively refining connections based on emergent properties. This paper introduces a novel algorithm, termed 'Adaptive Topology Evolution', which integrates reinforcement learning and graph theory to achieve automa...

HAIC: Heteroskedasticity-Adaptive Intrinsic Curiosity -Disentangling Epistemic Novelty from Aleatoric Noise in Non-Stationary Reinforcement Learning A Research Proposal

Neuromorphic Reinforcement Learning with Spiking Temporal Memory

This paper investigates the potential of neuromorphic reinforcement learning (RL) by integrating Spiking Temporal Memory (STM) networks. Traditional RL algorithms often struggle with complex, temporally extended tasks due to their reliance on explicit state representations and the challenges of handling noisy and async...

Self-Organizing Quantum Hardware via Adaptive Entanglement Control

This paper presents a novel approach to quantum hardware design and control, leveraging reinforcement learning to achieve self-organization and optimal performance. The core concept is that quantum processors, rather than relying on pre-determined calibration, can dynamically adapt their entanglement generation and mai...

Adaptive Neuron Topology Structure Optimization

This paper proposes a novel approach to neural network optimization centered around dynamically adapting the topology of the network through the adjustment of connections between neurons. The core idea is to create a neural network structure that is specifically tailored to a given task by leveraging reinforcement lear...