153 papers indexed here
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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 app...
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 a...
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...
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 an...
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 robus...
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 MAD...
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-...
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 algorith...
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 tra...
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 app...
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...
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 gra...
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