This paper investigates the application of reinforcement learning to automatically optimize lattice parameterization for complex systems, particularly in materials science and molecular modeling. The core claim is to develop a system that dynamically adjusts lattice parameters based on real-time feedback, aiming to improve a desired physical property through optimization. Traditional lattice parameterization methods are static and require manual adjustment, offering a dynamic optimization approach. This research explores the implementation of a reinforcement learning agent to learn optimal parameter settings, leading to improved physical property performance. The paper details the system architecture, the reinforcement learning agent's training process, and the evaluation of its performance on a representative dataset.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Traditional cognitive computing systems often struggle with robust reasoning and adaptability, particularly in dynamic and uncertain environments. This research proposes a novel approach to cognitive computing by integrating Bayesian belief networks (BBNs) and reinforcement learning (RL). BBNs provide a framework for representing prior knowledge, incorporating uncertainty, and performing deductive reasoning, while RL enables the system to learn optimal policies through interaction and reward maximization. The combined system leverages the strengths of both paradigms, allowing for a more adaptive, intelligent, and robust cognitive agent. We outline the core mechanisms, highlighting the synergy between knowledge representation via BBNs and policy learning through RL. The proposed architecture offers a pathway towards creating cognitive systems capable of handling complex, real-world scenarios where traditional methods fall short.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The design of effective feedback loops is a fundamental challenge in many engineering and scientific disciplines. Traditional approaches often involve manual tuning of parameters to achieve desired outcomes, which is a time-consuming and potentially error-prone process. This paper introduces the Self-Organizing Feedback Loop (SOFL) algorithm, a novel approach that leverages reinforcement learning to automatically optimize system parameters based on observed feedback. The core mechanism centers around a continuous reinforcement learning process that dynamically adjusts parameters to maximize desired outcomes, eliminating the need for manual tuning. This offers a significant advancement in system parameter control, reducing reliance on human intervention and enhancing system performance. The algorithm's design is based on a self-organizing structure, where parameters naturally adjust to achieve optimal conditions. We explore the theoretical foundations and practical implementation details of the SOFL, demonstrating its potential for automating parameter optimization and improving system performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to large-scale code analysis leveraging hierarchical abstraction learning. The core idea is to accelerate code analysis by learning hierarchical representations of code structures, effectively capturing semantic relationships across multiple levels of granularity. A reinforcement learning agent is employed to iteratively refine a hierarchical tree representation of the code, guided by metrics encompassing code complexity, data flow, and control flow. Unlike traditional static analysis methods, this approach dynamically adapts to the code's structure through learned abstractions, leading to improved efficiency and accuracy in code understanding. The proposed method offers a significant advancement in code analysis techniques, particularly for large and complex software systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to program code optimization leveraging reinforcement learning (RL). Traditional static code analysis techniques often fall short in achieving optimal performance due to their lack of adaptability and global optimization capabilities. Our proposed system utilizes an RL agent to dynamically adjust code optimization strategies based on observed program behavior and runtime characteristics. The agent learns through trial and error, receiving rewards for improved performance metrics (e.g., execution time, memory usage) and penalties for detrimental changes. The system's adaptive nature allows it to effectively tailor optimization strategies to diverse environments and codebases, potentially surpassing the limitations of static analysis methods. We detail the system architecture, the RL algorithm employed (specifically, a Q-learning variant), and the key components involved in the optimization process. The core claim is the construction of an adaptive system capable of automatically optimizing program code performance based on the running environment and code characteristics. The mechanism utilizes reinforcement learning to adjust optimization strategies based on runtime and performance indicators, achieving self-adaptive optimization. This research explores a new paradigm in code optimization, moving beyond static analysis to a dynamic, learning-based approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the problem of distributed resource management. Traditional approaches to this problem often rely on centralized control, which can be inefficient and vulnerable to single points of failure. This research proposes a decentralized solution utilizing MARL, where multiple agents, each responsible for managing a subset of resources, learn to coordinate and optimize overall system performance through interaction and reward signals. We formulate the resource management task as a multi-agent Markov Decision Process (MAMP), leveraging algorithms such as Independent Q-Learning (IQL) and Centralized Training with Decentralized Execution (CTDE) to train the agents. The core claim is that MARL offers a viable framework for distributed resource management. The mechanism relies on the agents learning optimal strategies through trial and error, adapting to changing conditions, and ultimately achieving greater efficiency and robustness compared to traditional methods. The novelty of this work lies in its exploration of MARL's potential in this domain, a field still in its nascent stages. The results demonstrate the feasibility and effectiveness of this approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel Relational Reinforcement Learning (RRL) framework designed to address the challenges of dynamic resource allocation in complex environments. Traditional Reinforcement Learning (RL) approaches often fall short when dealing with environments that constantly evolve and demand adaptive resource management. The core innovation lies in learning relational embeddings that capture the intricate dependencies between resources. The agent learns a state representation where resources are represented as points in a relational embedding space, allowing it to optimize allocation strategies based on these relationships. The reward function is explicitly designed to encourage the flow of resources between related entities, promoting efficiency and robustness. This approach represents a shift from purely individual resource optimization towards a systemic view, ultimately leading to more effective and resilient resource allocation systems. The framework is presented with detailed mathematical formulations and is intended to serve as a foundation for future research in dynamic resource management.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel dynamic topology optimization algorithm powered by quantum machine learning. Traditional topology optimization methods often require manual design of the topology, leading to limited flexibility and computational expense. Our algorithm leverages quantum machine learning to automatically learn and optimize the dynamic topology structure of data, resulting in significantly improved optimization efficiency. We propose a framework that utilizes quantum algorithms to represent and manipulate the data distribution, enabling adaptive topology adjustments throughout the optimization process. The core mechanism involves a quantum-enhanced representation of the data's topology, coupled with a reinforcement learning loop to dynamically adjust the topology based on feedback. We demonstrate the effectiveness of this approach through a series of benchmark problems, showcasing enhanced optimization speeds and improved solution quality compared to existing methods. The paper concludes with a discussion of the potential applications of this technology across diverse fields.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates a novel reinforcement learning algorithm utilizing adaptive reward function adjustment to enhance learning performance. Traditional reinforcement learning often relies on fixed reward functions, which can struggle to adapt to complex environments and unexpected situations. We propose a method that dynamically adjusts the reward function based on the learning process's inherent noise and uncertainty. This is achieved through a novel "adaptive adjustment" mechanism that continuously monitors and modifies the reward function in response to these factors. The core mechanism aims to mitigate the limitations of static reward functions, leading to improved sample efficiency and robustness in reinforcement learning. This work addresses the critical gap in current approaches by introducing a mechanism to dynamically tune the reward landscape, thereby improving the generalization capabilities of reinforcement learning agents.
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 presents the Non-Euclidean Geometry Synchronization Algorithm (NESA), a novel algorithm designed to automatically generate and validate geometric theorems within complex non-Euclidean geometries. The core aim is to provide a verifiable framework for mathematical proof and abstraction, moving beyond manual construction and leveraging the generative capabilities of a deep learning model. NESA utilizes a trained generative AI model to construct geometric constructions and proofs, creating a dynamically evolving system capable of discovering novel theorems. We detail the algorithm's architecture, training methodology, and the resulting validation process, emphasizing the potential for automated theorem generation and rigorous mathematical verification. The system's design incorporates a mechanism for continuous refinement based on established geometric principles, ensuring the generated theorems are demonstrably valid.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to optimize software development processes. Traditional software development methodologies often struggle with adaptability and efficiency, particularly in complex projects. This research proposes a novel approach leveraging MARL to dynamically adjust and refine the development workflow. The core idea involves modeling the software development process as a multi-agent system, where each agent represents a distinct stage or activity. These agents learn optimal strategies through interaction and reward signals, leading to improved development efficiency and quality. We present a framework for formulating this problem, detailing the agent architecture, state space, action space, and reward function. The effectiveness of the MARL approach is demonstrated through a theoretical analysis and conceptual design, highlighting its potential to overcome limitations of static, rule-based methodologies. Future work will focus on implementing and testing this framework within a simulated software development environment.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
