This paper proposes a novel approach to system management leveraging dynamic topological information for intelligent inference. The core idea is to construct and maintain a semantic network that reflects the evolving relationships between system components, considering factors like network latency, device load, and communication costs. This network is continuously updated using a reinforcement learning-based probabilistic model, integrated with causal inference and knowledge graphs. The model learns and adapts to changes in the system topology, enabling real-time state prediction, anomaly detection, and optimized resource allocation. The system's performance is evaluated through simulation, demonstrating the effectiveness of the proposed methodology. The key contribution lies in treating topology as a dynamic element within the semantic reasoning process, moving beyond static parameter assumptions. The system utilizes a core claim of dynamic topology information to build and update a semantic network. The core mechanism involves a reinforcement learning-based probabilistic model combined with causal inference and knowledge graphs.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of multi-agent reinforcement learning (MARL) for optimizing complex systems. Traditional system optimization approaches often rely on centralized control or distributed control strategies, which can struggle with the inherent complexity and dynamic nature of many real-world systems. This research proposes a novel framework utilizing a population of intelligent agents trained through MARL to autonomously optimize these systems. The core concept involves decomposing the complex system into multiple agents, each responsible for optimizing a specific sub-objective. These agents interact through a combination of cooperation and competition, ultimately leading to overall system optimization. We explore the theoretical foundations of this approach, outlining the key components and the learning dynamics involved. The paper demonstrates the potential of MARL to overcome the limitations of conventional methods, offering a more adaptive and robust solution for complex system optimization problems. The effectiveness of this approach is discussed through a theoretical analysis and a conceptual design, paving the way for future research and practical implementations. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to swarm control that leverages collective intelligence to achieve adaptive behavior. Traditional swarm control methods often rely on pre-defined rules and lack the flexibility to respond to dynamic environmental changes or unexpected events. This work addresses this limitation by constructing a control system for swarms where individual agents learn and adapt their behavior through interactions with their peers, informed by Bayesian inference and reinforcement learning. The core idea is to create a decentralized system where collective knowledge emerges, enabling the swarm to optimize its performance in complex and unpredictable scenarios. The system is designed to handle uncertainties and adapt to evolving task requirements. Mathematical formulations are provided to illustrate the key components of the control architecture, including agent interaction models, Bayesian inference processes, and reinforcement learning algorithms. This approach represents a significant step towards truly adaptive and robust swarm control, with potential applications in robotics, autonomous systems, and other areas where adaptability is paramount.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Deep learning models often exhibit sensitivity to training data, leading to suboptimal performance. Traditional regularization techniques, while effective, can be inflexible and require extensive hyperparameter tuning. This paper introduces an adaptive topology for deep learning model regularization that dynamically adjusts the connectivity patterns within a neural network based on the characteristics of the training data. We propose a reinforcement learning-based algorithm to automatically optimize the topology, resulting in improved generalization and robustness. The core mechanism leverages the concept of a dynamically evolving network structure to mitigate the effects of data heterogeneity. The proposed approach offers a novel and potentially transformative solution for enhancing deep learning model performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel self-adaptive reinforcement learning algorithm based on topological networks. Traditional reinforcement learning approaches often rely on predefined reward functions and static policies, limiting their ability to adapt to dynamic environments. Our algorithm leverages the inherent topological structure of the environment to enable more intelligent and adaptive decision-making. We propose a method for constructing a topological representation of the environment, employing a hierarchical representation to capture complex relationships and dependencies. The core mechanism involves a dynamically adjusted policy based on this topological structure, allowing the agent to efficiently explore and react to changes in the environment. We demonstrate the effectiveness of this algorithm through several illustrative scenarios, showcasing its ability to surpass traditional reinforcement learning methods in terms of sample efficiency and robustness. The proposed algorithm provides a promising avenue for developing more sophisticated and adaptable reinforcement learning agents capable of navigating complex and evolving environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to neural network optimization that dynamically adapts the network topology based on learned dependencies. The core idea is to leverage reinforcement learning, where two agents collaborate: one adjusts connection weights and the other modifies the network's structure (adding, removing, or restructuring connections). A 'dependency graph' guides the agents' decisions, reflecting the learned information dependencies between neurons. This dynamic adaptation addresses the limitations of traditional methods that assume a fixed network topology, particularly when dealing with complex dependencies and non-Euclidean data. The system aims to achieve more efficient training and improved generalization performance by allowing the network to evolve its structure to better represent the underlying data. The optimization process is driven by minimizing a loss function, and the dependency graph is continuously updated based on the error signal. The key contribution lies in the integration of topology adaptation with reinforcement learning, providing a framework for creating inherently adaptive and robust neural networks. The proposed methodology demonstrates potential for significant improvements in network performance across various domains.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel cognitive architecture for embodied artificial intelligence (AI) systems, designed to address the limitations of current approaches. The architecture leverages principles of cognitive science, specifically Bayesian inference, hierarchical control, and reinforcement learning, to facilitate robust learning and adaptation within complex, dynamic environments. The core claim is that existing embodied AI systems lack a holistic cognitive architecture capable of seamlessly integrating perception, action, and learning. The developed architecture aims to overcome this deficiency by providing a structured framework for representing knowledge, planning actions, and updating beliefs based on sensory input and interaction. Key components include a Bayesian inference engine for probabilistic reasoning, a hierarchical control system for managing complex behaviors, and reinforcement learning algorithms for optimizing actions and achieving goals. This integrated approach promises to significantly enhance the capabilities of embodied AI agents, enabling them to navigate, learn, and interact with the world in a more intelligent and adaptive manner. The architecture is presented as a modular system, allowing for flexibility and extensibility as the field of embodied AI continues to evolve. This document outlines the architectural design, the underlying principles, and the anticipated benefits of this approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of adaptive chaos theory, leveraging reinforcement learning to dynamically adjust parameters of chaotic systems. Traditional approaches often rely on fixed, static parameter sets, limiting the system's flexibility and potential for robust behavior. This research introduces a novel system that employs a reinforcement learning agent to iteratively refine parameters based on real-time feedback, offering a paradigm shift towards intelligent parameter control. The core mechanism centers around a feedback loop that continuously evaluates the system's response to changing conditions, optimizing the parameters to achieve desired outcomes. The goal is to move beyond static parameter sets and towards a system capable of adapting to complex, evolving environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to digital circuit synthesis leveraging the power of Evolutionary Reinforcement Learning (ERL). Traditional circuit synthesis methods often rely on deterministic algorithms that struggle to effectively manage the complexity and dynamism inherent in modern design requirements. This research addresses this limitation by implementing an ERL system capable of iteratively generating and refining circuit designs. The core of the system involves an evolutionary algorithm that explores the design space, guided by a reinforcement learning agent that dynamically learns a reward function based on design rules and constraints. This dynamic reward function enables the system to adapt to evolving design priorities and optimize for performance metrics such as delay, power consumption, and area. The resulting system demonstrates the potential to overcome the drawbacks of conventional synthesis techniques, offering a more robust and adaptive solution for complex circuit design problems. The key innovation lies in the synergistic combination of evolutionary search and reinforcement learning, creating a system that can autonomously learn and refine circuit designs within highly constrained and dynamic environments. The system is designed for adaptability and efficiency, and the potential for its application in various digital design domains is significant.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This research investigates the application of reinforcement learning to automatically generate and optimize geometric patterns, focusing on identifying novel patterns exhibiting complex, self-organizing behavior. Traditional geometric pattern generation often relies on predefined rules, limiting the potential for truly creative and responsive designs. We propose a novel approach leveraging reinforcement learning to explore the vast space of possible patterns, rewarding patterns that demonstrate emergent complexity and self-organization. The core mechanism centers on using a reinforcement learning agent to iteratively refine patterns based on feedback, leading to the discovery of aesthetically pleasing and functionally relevant geometric forms. This work aims to advance computational geometry by providing a method for automated pattern design, pushing the boundaries of what is possible with algorithmic creativity.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of reinforcement learning to dynamically optimize topology in parallel computing systems. The core claim is to develop a system capable of automatically adjusting the topology of parallel environments—specifically, the number of threads and processes—to maximize throughput and minimize latency, driven by workload characteristics. We propose a novel reinforcement learning agent trained to learn optimal topology configurations based on real-time workload analysis. This system aims to overcome the limitations of traditional manual tuning methods, offering a more adaptive and responsive approach to parallel system design. The research will detail the architecture of the agent, the training process, and preliminary results demonstrating its effectiveness in a simulated environment.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces the Neuro-Symbolic Symbiosis Architecture (NSSA), a novel framework designed to enhance reasoning and learning capabilities by dynamically adjusting the interaction between neural networks and symbolic knowledge bases. The core claim is that by modulating the interaction strength between these two components, we can achieve more efficient and robust inference. NSSA employs a multi-layered architecture comprising a neural inference layer for initial pattern recognition and representation learning, a symbolic knowledge base layer for storing structured knowledge, and a symbiotic engine—a reinforcement learning-based controller—to dynamically adjust the flow of information between these layers. The engine's objective is to minimize inference error while maximizing knowledge base utilization. Unlike existing neuro-symbolic methods that often rely on static structures or fixed interaction methods, NSSA leverages reinforcement learning for a highly dynamic and adaptive symbiotic relationship, potentially leading to improvements in generalization, interpretability, and efficiency. The presented architecture offers a shift away from the traditional "neural network vs. symbolic knowledge" dichotomy, emphasizing the mutual dependence and synergistic interaction between the two representation paradigms.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
