This paper investigates the application of adaptive reinforcement learning (RL) for efficient hardware resource management. Traditional hardware resource management often relies on static configurations and manual tuning, which can be inefficient and unresponsive to dynamic workloads. This research proposes a novel approach utilizing RL to autonomously learn and optimize resource allocation and scheduling policies. The core idea is to model the hardware resource management problem as an RL problem, where an agent interacts with the environment (hardware resources) and learns through trial and error to maximize a defined reward function. The presented framework offers the potential for significant improvements in resource utilization, reduced latency, and enhanced overall system performance. Specifically, we explore the application of multi-agent reinforcement learning to manage CPU, memory, and network bandwidth simultaneously, addressing the complexities of modern heterogeneous systems. The evaluation of the proposed method demonstrates its effectiveness in adapting to varying workloads and achieving superior resource allocation compared to conventional methods. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of reinforcement learning (RL) to the problem of adaptive model selection within deep learning. Traditional model selection methods often rely on manual tuning or grid searches, which can be computationally expensive and inefficient. We propose a framework where a reinforcement agent learns to dynamically select the optimal deep learning model architecture and hyperparameters based on the observed performance. The agent's state space represents the current model configuration, and the action space comprises the available architectural choices and hyperparameter settings. The reward function is defined based on the validation performance of the selected model. Through extensive simulations, we demonstrate that our RL-based approach can effectively identify high-performing models and significantly reduce the time and resources required for model selection compared to conventional methods. The core claim is that reinforcement learning can be used to dynamically select the best deep learning model for a given task and dataset. The core mechanism involves training a reinforcement agent to explore the space of deep learning architectures and hyperparameters, learning to choose models that maximize performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper addresses the critical challenge of concept drift in continuous learning systems. Traditional machine learning approaches often assume a static environment, leading to performance degradation when the underlying data distribution changes over time. This work proposes a novel framework for continuous learning that proactively predicts concept drift and dynamically adapts the learning strategy. The core of the system lies in a combined approach utilizing time series analysis and neural networks for drift detection, coupled with reinforcement learning for optimizing learning policies such as transfer learning and meta-learning. We demonstrate the effectiveness of this framework through a theoretical analysis and outline a potential implementation strategy. The goal is to create a system capable of maintaining long-term learning effectiveness in dynamic environments. The primary contribution is a holistic approach to continuous learning, shifting the focus from algorithm optimization to proactive drift prediction and adaptive learning strategies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel adaptive differential privacy (DP) mechanism designed to mitigate the limitations of traditional DP approaches, particularly concerning excessive noise addition in scenarios with small datasets. The core idea is to dynamically adjust the noise scale based on the sensitivity of the query and the current state of the data. This is achieved through the integration of a reinforcement learning (RL) agent that learns optimal thresholds for noise addition. The RL agent's objective is to minimize privacy loss while simultaneously preserving data utility. We demonstrate that this dynamic thresholding approach offers a significantly improved trade-off between privacy and utility compared to fixed noise scales commonly employed in DP. The theoretical analysis provides insights into the convergence properties of the RL agent and the overall privacy guarantees offered by the mechanism. The proposed method represents a crucial step towards more efficient and effective DP implementations, particularly in resource-constrained settings where minimizing noise is paramount.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Deep Reinforcement Learning (DRL) has achieved remarkable success in various domains, including game playing and robotics. However, the inherent uncertainty and complexity of DRL policies pose significant challenges to safety and reliability. This work introduces a novel approach to formally verify the safety and robustness of DRL policies using abstract interpretation. We leverage abstract domains to represent the learned value function, enabling the detection of potential safety violations without requiring actual execution of the policy. The core idea is to transform the continuous value function, typically output by a DRL agent, into a discrete abstract representation. This allows us to apply well-established abstract interpretation techniques to identify potential issues like out-of-bounds access, negative rewards, or violations of specified constraints. The technique offers a practical method for assuring the correctness of DRL policies, particularly in safety-critical applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel decentralized multi-agent reinforcement learning (MARL) algorithm designed to address the challenges posed by environmental uncertainty and information asymmetry in multi-agent systems. The core of the algorithm lies in leveraging communication between agents, inspired by approaches like MADDPG, to facilitate information sharing and collaborative learning. We demonstrate that this communication-based strategy significantly enhances the performance of multi-agent systems, particularly when dealing with stochastic environments where individual agent observations are incomplete and unreliable. The algorithm incorporates a novel uncertainty estimation module to dynamically adjust the communication frequency and content, optimizing for efficiency and robustness. We formalize the problem as a Partially Observable Markov Decision Process (POMDP) and outline the key components of the proposed solution, focusing on the decentralized training and execution strategy. Experimental results (simulated) on benchmark MARL environments illustrate the effectiveness of the proposed method compared to traditional centralized and decentralized approaches. The key contributions of this work are a communication-augmented MARL framework that addresses uncertainty and information asymmetry and a practical approach for decentralized learning and execution in complex, dynamic environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach, Neural Network Adaptive Topology Optimization (NNAUTO), for designing and optimizing neural network architectures. The core idea is to dynamically adjust the topology of a neural network during training using a reinforcement learning (RL) agent, guided by a physics-informed energy-based optimization framework. Traditional neural network design often relies on fixed architectures or static designs, limiting adaptability to complex datasets and diverse tasks. NNAUTO addresses this limitation by enabling the network to evolve its structure in response to learning progress, optimizing for both accuracy and efficiency. The system employs an RL agent that learns optimal topology adjustments based on network performance metrics – such as accuracy and latency – and the corresponding changes in network topology, encompassing connection strengths and neuron counts. A key element is the integration of an energy-based optimization method, specifically a potential field approach, which constraints the learning process, ensuring that the generated topology modifications are physically plausible and aligned with biological neural network principles. This dynamic adaptation leads to significant improvements in network performance and efficiency compared to static or post-training architectural adjustments. The presented methodology offers a pathway towards more robust and adaptive neural networks, particularly for applications involving highly variable or complex input data.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to digital signal processing (DSP) utilizing reinforcement learning (RL). Traditional DSP algorithms are frequently designed with static parameters, rendering them ineffective when faced with dynamic or non-stationary signals. The proposed method leverages RL to train a DSP algorithm, allowing it to adapt its operational parameters in real-time based on incoming signal data. This dynamic adaptation aims to optimize performance metrics such as signal-to-noise ratio (SNR), root mean square error (RMSE), or spectral accuracy. The core mechanism involves an RL agent interacting with a simulated DSP environment, learning through trial and error to minimize a defined loss function related to the desired signal processing outcome. The framework is demonstrated conceptually, outlining the key components and potential benefits. Future research will focus on developing specific RL algorithms and evaluating the system's performance against established DSP techniques.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Existing graph optimization algorithms are fundamentally static, failing to adapt effectively to the inherent dynamic nature of graph structures. This paper introduces a novel Dynamic Graph Optimization Algorithm (DGOTA) designed to address this limitation. The core of DGOTA leverages Reinforcement Learning (RL) to train a dynamic optimization strategy. This strategy is continuously monitored by a sensor network that provides real-time state information about the graph. Based on this state data, the RL agent dynamically adjusts the optimization parameters, ensuring optimal performance under fluctuating graph conditions. The proposed algorithm offers significant advantages over traditional static methods, providing a robust and adaptive solution for graph optimization problems. This work demonstrates a new approach to graph optimization, capable of better handling the complexities of dynamic graph environments. The key contributions are the RL-based adaptive strategy and the sensor-driven state monitoring, leading to a more responsive and effective optimization process.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of adaptive reinforcement learning (RL) for efficient hardware resource management. Traditional hardware resource management often relies on static configurations and manual tuning, which can be inefficient and unresponsive to dynamic workloads. This research proposes a novel approach utilizing RL to autonomously learn and optimize resource allocation and scheduling policies. The core idea is to model the hardware resource management problem as an RL problem, where an agent interacts with the environment (hardware resources) and learns through trial and error to maximize a defined reward function. The presented framework offers the potential for significant improvements in resource utilization, reduced latency, and enhanced overall system performance. Specifically, we explore the application of multi-agent reinforcement learning to manage CPU, memory, and network bandwidth simultaneously, addressing the complexities of modern heterogeneous systems. The evaluation of the proposed method demonstrates its effectiveness in adapting to varying workloads and achieving superior resource allocation compared to conventional methods. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 maintenance through real-time feedback. We propose a decentralized control system where reinforcement learning algorithms continuously optimize control parameters – such as laser pulse shapes and magnetic field strengths – based on metrics like entanglement fidelity and coherence time. This adaptive strategy mitigates the challenges associated with traditional, fixed-parameter control schemes, which often struggle to account for the inherent noise and fluctuations in quantum systems. The resulting architecture represents a significant step towards truly autonomous quantum computing, promising enhanced scalability and robustness. This paper details the theoretical framework, the reinforcement learning algorithm employed, and outlines the potential impact of this approach on future quantum hardware development.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to Deep Graph Reinforcement Learning (DGRL) that leverages hierarchical policy learning to enhance both sample efficiency and learning speed. Traditional DGRL methods often suffer from slow exploration and high computational demands, particularly when dealing with large, complex graphs. Our proposed framework addresses these challenges by decomposing the reinforcement learning problem into a hierarchy of policies. Higher-level policies define abstract goals, while lower-level policies execute actions to achieve those goals. This hierarchical structure facilitates more focused exploration, reduces the search space, and ultimately accelerates convergence. We demonstrate the effectiveness of this approach through theoretical analysis and a comprehensive evaluation. The core claim of this work is that hierarchical policy learning can significantly improve the sample efficiency and learning speed of deep graph reinforcement learning algorithms. The key mechanism is the decomposition of the learning problem into a hierarchy of policies, allowing for more efficient exploration and faster convergence.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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