127 papers indexed here
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This paper addresses the challenge of optimizing multi-agent collaborative learning (MCL) by introducing a dynamic topology structure optimization framework. Traditional MCL methods often rely on fixed topologies, which may not be optimal for varying task demands and evolving agent states. We propose a novel approach t...
This paper proposes a novel approach to Neural Architecture Search (NAS) termed Dynamic Topological Adaptive NAS (DTANAS), which leverages concepts from topological data analysis and physical systems to achieve more efficient and robust architecture optimization. The core idea is to model the search space as a complex...
This paper explores the development of adaptive quantum algorithms designed to optimize the performance of quantum algorithms. The core concept involves dynamically adjusting parameters such as qubit coupling strength and measurement time, based on the algorithm's runtime behavior. This is achieved through the applicat...
This paper presents a novel approach to neural network design utilizing self-organizing principles to dynamically learn network topology. The core idea involves constructing a neural network architecture capable of adapting its structure in response to incoming data and its internal state. This adaptation is achieved t...
Deep Reinforcement Learning (DRL) has demonstrated remarkable success in solving complex control problems. However, deploying DRL agents in real-world scenarios, particularly in safety-critical applications, presents significant challenges. The inherent stochasticity and complex decision-making processes of DRL policie...
The efficient scheduling of distributed machine learning (ML) jobs presents a significant challenge due to the complex interplay of factors such as varying job resource requirements, heterogeneous computing environments, and dynamic workload fluctuations. Traditional scheduling approaches often rely on heuristics or si...
This paper proposes a novel recursive learning system (BRRLS) that leverages biofeedback to enable adaptive learning. The core idea is to utilize real-time monitoring of physiological signals, such as heart rate and electroencephalography (EEG), to construct a system capable of dynamically adjusting its learning strate...
This paper proposes a novel hybrid framework integrating the Simulated Evolution Algorithm (SEA) and Deep Reinforcement Learning (DRL) to tackle complex optimization problems. The core idea is to leverage SEA's global search capability for initial exploration and DRL's local optimization prowess for refining solutions....
This paper introduces a novel approach to program understanding and optimization, termed Dynamic Semantic Topology Learning and Program Reasoning. The core claim is that program behavior is not solely determined by its source code, but significantly influenced by the dynamic semantic relationship network within its run...
This paper explores the application of probabilistic programming to the field of model-based reinforcement learning (MBRL). Traditional MBRL approaches often rely on deterministic models, which can be brittle and fail to adequately represent the inherent uncertainty in real-world environments. We argue that leveraging...
This paper explores the development of an algorithmic chaos theory system designed to generate complex, multi-dimensional chaos patterns without relying on predefined, fixed parameters. Leveraging a reinforcement learning-inspired approach, the algorithm continuously adjusts its internal parameters based on observed sy...
This paper explores the potential of quantum computing to revolutionize Monte Carlo Tree Search (MCTS) algorithms, a cornerstone technique in reinforcement learning and game AI. The core claim is that by harnessing quantum superposition and interference, we can significantly accelerate MCTS's exploration of complex opt...
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