159 papers indexed here
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This paper explores the application of quantum self-organized networks (QSON) reinforcement learning, leveraging quantum entanglement and coherence to enhance learning strategies and improve the performance of reinforcement learning agents. Traditional reinforcement learning relies on classical computers, while this re...
This paper proposes a novel approach to deep learning model compression called Adaptive Precision Quantization (APQ). The core idea is to dynamically adjust the quantization precision of individual layers or parameters during training, guided by a reinforcement learning agent. Unlike traditional fixed-point quantizatio...
Quantum error correction (QEC) is a crucial step towards fault-tolerant quantum computation. However, traditional QEC schemes often rely on fixed, pre-determined parameters, which may not be optimal for all quantum computations. This paper proposes a novel approach to QEC that leverages adaptive learning algorithms to...
This paper presents a novel approach to computational graph optimization utilizing reinforcement learning (RL). Traditional static optimization methods often fail to adequately address the dynamic and evolving resource constraints encountered in modern computing environments. We propose a system where a reinforcement l...
The increasing complexity of software systems poses significant challenges in managing dependencies. Traditional dependency analysis tools struggle to adapt to the dynamic changes in topology, hindering compile-time optimization and runtime performance. This paper introduces the Dynamic Topology-Dependent Graph Optimiz...
This paper presents an innovative reinforcement learning algorithm designed to enhance the performance of chaotic systems through the integration of adaptive chaos algorithms. The core of this approach lies in employing reinforcement learning to train an adaptive chaos algorithm that dynamically adjusts its parameters...
This paper presents a novel dynamic adaptive optimization framework combining reinforcement learning with a dynamically adjusting optimization strategy. The core focus is on optimizing complex, non-structured problems, leveraging reinforcement learning to learn an optimized objective function and dynamically adjust the...
Universal Constraint Induction (UCI) presents a novel framework for automatically generating and verifying constraints for complex dynamical systems, facilitating the creation of entirely new and unpredictable system behavior. This work introduces a constraint-discovery algorithm leveraging Generative Adversarial Netwo...
Reinforcement learning (RL) relies heavily on dynamic programming (DP) to find optimal policies. However, the curse of dimensionality significantly limits its applicability in environments with large or continuous state spaces. Traditional DP methods become computationally intractable, often requiring exponential time...
Distributed deep learning has emerged as a crucial paradigm for training increasingly complex models, often necessitating the partitioning of computational graphs across multiple devices. However, traditional graph partitioning techniques rely on static heuristics, failing to adapt to the dynamic and evolving workload...
Universal Constraint Induction (UCI) presents a novel framework for automatically generating and verifying constraints for complex dynamical systems, facilitating the creation of entirely new and unpredictable system behavior. This work introduces a constraint-discovery algorithm leveraging Generative Adversarial Netwo...
Reinforcement learning (RL) relies heavily on dynamic programming (DP) to find optimal policies. However, the curse of dimensionality significantly limits its applicability in environments with large or continuous state spaces. Traditional DP methods become computationally intractable, often requiring exponential time...
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