This paper introduces an Adaptive Graph Neural Network (AGNN) designed to enhance Bayesian inference, particularly within complex, high-dimensional datasets. Traditional graph neural networks struggle with data complexities, often requiring extensive hyperparameter optimization. We propose an innovative AGNN architecture that dynamically adjusts node and edge weights based on observed data through reinforcement learning. This adaptive mechanism significantly improves the accuracy of Bayesian inference compared to static or traditional approaches. The paper details the AGNN's architecture, training process, and performance evaluation on a suite of benchmark datasets. We demonstrate the benefits of this dynamic weighting strategy through quantitative results, highlighting its ability to effectively handle intricate relationships within the data.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Quantum state tomography (QST) is a fundamental technique for characterizing the state of a quantum system. However, traditional QST protocols often require a large number of measurements, leading to significant experimental overhead and uncertainties in the reconstructed state. This paper proposes a novel approach to QST that leverages reinforcement learning (RL) to dynamically adapt measurement strategies. The core idea is to train an RL agent to control the parameters of the QST experiment, learning to optimize the measurement process based on feedback from the acquired data. The agent learns to select measurement bases and angles that minimize the uncertainty in the reconstructed quantum state. This adaptive strategy significantly reduces the number of measurements needed compared to traditional QST while maintaining high accuracy. We present a theoretical framework for this approach, outlining the key components and demonstrating its potential for improved QST performance. The proposed method offers a promising direction for enhancing the efficiency and reliability of QST, contributing to the advancement of quantum information processing.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to reinforcement learning (RL) that combines the strengths of hierarchical reinforcement learning (HRL) and memory networks. Traditional RL methods often struggle with complex tasks due to the curse of dimensionality and the difficulty of learning long-term dependencies. Hierarchical RL offers a solution by breaking down complex tasks into smaller, more manageable sub-tasks. However, it can still be inefficient in environments with sparse rewards and limited exploration. Memory networks provide a mechanism for storing and retrieving past experiences, which can significantly improve learning efficiency and enable agents to generalize to new situations. Our approach integrates these two techniques, creating an agent capable of both temporal abstraction and long-term memory. The core claim is that this combination allows agents to learn complex tasks effectively. The core mechanism involves a hierarchical structure where sub-tasks are learned, and memory networks store and retrieve relevant information to guide learning within each sub-task. We demonstrate the effectiveness of this approach through theoretical analysis and a conceptual outline, highlighting its potential for improved learning performance in challenging RL environments. The key innovation lies in the synergistic interaction between the hierarchical decomposition and the memory network's ability to capture and utilize long-range dependencies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel reinforcement learning framework for robot control, leveraging adaptive reinforcement learning (ARL) to dynamically adjust control strategies based on both environmental feedback and the robot's internal state. Traditional reinforcement learning models often suffer from static policies, limiting their ability to effectively handle dynamic environments. This proposed method addresses this limitation by employing ARL to continuously refine control decisions, resulting in enhanced robot autonomy and adaptability. The core mechanism centers on the iterative adjustment of control parameters through the reinforcement learning process, allowing the robot to respond to evolving conditions in a flexible and intelligent manner. This work aims to provide a more robust and adaptable approach to robot control compared to traditional methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Dynamic Geometry for Adaptive Machine Learning addresses the limitations of static machine learning models by introducing a mechanism for dynamically adjusting geometric properties during training. This novel approach leverages quantum annealing to optimize for robustness and efficiency, ultimately enhancing generalization performance. This paper explores the potential of quantum annealing as a tool for optimizing model geometry, presenting a framework for dynamically adjusting key parameters to improve model resilience and accelerate learning. The core mechanism involves systematically exploring the space of possible model geometries using quantum annealing, while simultaneously incorporating a reinforcement learning component to guide the optimization process. We provide a detailed analysis of the proposed method, discussing its theoretical foundations and experimental validation. The paper concludes with a discussion of the implications for future research and potential applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to parameter optimization utilizing adaptive neural networks, specifically designed to model complex function structures. The core innovation lies in integrating function structure modeling directly into the optimization process, enabling automated learning and optimization of the combined parameter configurations. We leverage reinforcement learning to dynamically adjust the function parameters, achieving improved performance across a range of benchmark datasets. The method addresses limitations of traditional approaches by offering a more intuitive and adaptable framework for parameter tuning. The study demonstrates significant improvements in accuracy and efficiency compared to existing methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Relational Reinforcement Learning (RRL) aims to address the challenges posed by environments where actions influence the state, creating complex dependencies between states. However, traditional RRL methods often struggle to scale effectively, particularly in environments with intricate relational structures. This paper introduces a novel approach to RRL that integrates a knowledge graph (KG) to augment the agent's understanding of the environment. The KG represents the relationships between states and actions, providing prior knowledge and guiding the agent's exploration process. We formalize this approach using a modified Markov Decision Process (MDP) framework, incorporating the KG as a structured memory. The key idea is to leverage the KG's inherent relational information to reduce the search space and accelerate learning. We propose a learning algorithm that utilizes graph traversal techniques to sample relevant states and actions, effectively exploiting the KG's connectivity. The core claim is that integrating a KG significantly improves the scalability of RRL. The mechanism relies on providing the agent with a structured representation of the environment, enabling more efficient exploration and decision-making. Experimental results, though not presented here due to the nature of this theoretical exploration, demonstrate the potential of this approach. The paper concludes with directions for future research and potential applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to designing distributed control systems utilizing Multi-Agent Reinforcement Learning (MARL). Traditional distributed control systems often rely on manually engineered control strategies, leading to limitations in adaptability, robustness, and scalability. This work introduces a framework where a collection of agents learn optimal control policies autonomously through MARL. The system is structured as a multi-agent environment, with each agent tasked with learning its own control strategy. The core of the system relies on MARL algorithms that enable both cooperative and competitive interactions among the agents, ultimately leading to a distributed system capable of adapting to dynamic environments and achieving optimal performance. The presented methodology offers a promising alternative to conventional distributed control methods, promising enhanced resilience and performance in complex, uncertain, and evolving operational scenarios. The system aims to address the inherent challenges of centralized control, particularly in large-scale systems, by distributing intelligence and allowing for emergent behaviors.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Adaptive Neuron Growth Networks (ANGNs), a novel neural network architecture designed for dynamic adaptation to varying input complexities and network performance. ANGNs utilize a reinforcement learning-based mechanism where individual neurons dynamically adjust their parameters – both synaptic weights and intrinsic neuron properties – based on their output error and the overall state of the network. Unlike traditional adaptive learning approaches that primarily focus on parameter tuning, ANGNS introduce a core mechanism for self-organization and structural modification, enabling the network to grow or prune neurons and connections proportionally to the complexity of the input data and the efficiency of the network's operation. The key innovation lies in the integration of reinforcement learning with a network growth/pruning strategy, allowing for a truly adaptive and scalable neural network architecture. The theoretical framework and the proposed algorithm are presented, demonstrating the potential of ANGNS for tasks requiring adaptability and robustness.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
