This paper introduces an adaptive constraint-based fractal generation system designed to produce visually compelling and optimized fractal images. The core innovation lies in a reinforcement learning algorithm that dynamically adjusts fractal parameters based on feedback from the input data, allowing for continuous optimization of visual characteristics. Traditional fractal algorithms often rely on pre-defined parameters, limiting the potential for nuanced and aesthetically pleasing results. This work addresses this limitation by implementing a self-learning mechanism that iteratively refines fractal generation based on the input data. The system's architecture incorporates a reinforcement learning agent trained to maximize a defined perceptual quality metric. The paper details the system's components, training methodology, and preliminary results demonstrating the system's ability to generate fractals with improved visual quality. Furthermore, a comprehensive analysis of the algorithm's performance reveals the benefits of adaptive constraint-based generation in achieving desired aesthetic characteristics. The paper concludes with a discussion of future research directions, including exploring more sophisticated reinforcement learning techniques and integrating the system with other generative art tools.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Quantum circuit compilation is a crucial step in realizing quantum algorithms on real quantum hardware. Traditionally, compilation methods are largely static, relying on pre-defined mappings and optimizations that fail to account for the inherent noise and variability present in quantum devices. This paper proposes a novel approach leveraging Reinforcement Learning (RL) to dynamically generate and optimize quantum circuits. An RL agent is trained to learn a policy for circuit design, adapting to specific hardware characteristics and real-time measurement data. The agent's goal is to minimize the overall error rate of the compiled circuit. This dynamic compilation framework offers a feedback loop between hardware performance and circuit design, resulting in adaptive and robust quantum computation. The core claim of this work is that traditional static compilation methods are insufficient, and dynamic compilation driven by RL offers a superior solution. The approach presented here represents a significant advancement towards realizing the full potential of quantum computing by mitigating the effects of hardware imperfections. The framework includes a detailed description of the RL agent architecture, the reward function design, and the exploration strategy employed. The effectiveness of the proposed method is demonstrated through simulations, highlighting its ability to outperform static compilation strategies in noisy environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel framework for intelligent learning and decision-making in complex environments by fusing probabilistic graphical model learning with reinforcement learning. The core idea is to construct a dynamic system capable of learning both the structure and parameters of probabilistic graphical models, coupled with a reinforcement learning algorithm to optimize the agent's behavior. A neural network-based probabilistic graphical model learner is designed to adaptively adjust the model's structure and parameters based on environmental feedback. This learner then collaborates with a reinforcement learning algorithm to refine the agent's policy. The proposed approach offers a new paradigm for intelligent systems, allowing them to autonomously learn and adapt to dynamic environments. The key contribution lies in the synergistic combination of these two powerful techniques, leading to improved learning efficiency and decision quality. The framework is presented with detailed mathematical formulations and the potential for future development is discussed.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The design of neural networks for deployment on specialized hardware is a significant bottleneck in the widespread adoption of deep learning. Traditional methods often rely on manual tuning or computationally expensive search algorithms, failing to effectively account for the unique constraints and opportunities presented by different hardware platforms. This research proposes a novel approach utilizing reinforcement learning (RL) to automate the process of hardware-aware neural network design. The system learns to optimize network architectures directly by interacting with a simulated hardware environment, receiving rewards based on the network's performance on that specific hardware. The core of the method involves an RL agent that explores the design space of neural networks – considering factors like layer types, number of layers, and connection topologies – guided by performance metrics such as latency and power consumption. This work demonstrates the feasibility and effectiveness of using RL for this complex optimization problem, offering a potentially transformative solution for bridging the gap between neural network design and hardware execution. The key contributions include a framework for modeling hardware constraints within an RL environment and a methodology for learning optimal network architectures that are specifically tailored to the target hardware.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to online anomaly detection utilizing adaptive Bayesian Networks (ABNs). Traditional Bayesian Networks (BNs) often fail to effectively handle the dynamic nature of real-world data streams, leading to inaccurate anomaly detection. Our approach overcomes this limitation by dynamically updating the conditional probability tables within a BN using a reinforcement learning (RL) algorithm. The RL agent is guided by anomaly signals, allowing the network to continuously learn and adapt to the evolving underlying data distribution. We define a formal framework for this adaptive BN, outlining the key components and their interactions. The core concept involves constructing a BN where node parameters, specifically the conditional probabilities, are continuously refined. This creates a self-adapting system capable of identifying deviations from the learned "normal" behavior. We demonstrate the feasibility and effectiveness of this approach through a theoretical analysis and provide a detailed description of the algorithm. The resulting adaptive system offers improved responsiveness and accuracy compared to static BNs in online anomaly detection scenarios. The system's ability to learn and adjust its internal parameters based on incoming data makes it particularly well-suited for environments where the normal behavior is not stationary. The primary contribution lies in the synergistic combination of Bayesian inference and reinforcement learning, providing a robust and adaptable solution for real-time anomaly detection.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This research proposes a novel approach to neural network design centered around dynamic topology adaptation. The core concept leverages the principles of biological neural networks, specifically incorporating dynamic synaptic plasticity and connection modulation, to create networks capable of real-time adaptation to complex data streams. The system utilizes a microfluidic-based neuromorphic chip with programmable physical synapses, coupled with reinforcement learning algorithms to optimize network topology. The primary goal is to overcome the limitations of traditional static neural network architectures by enabling a truly adaptive system. The resulting network demonstrates enhanced efficiency in information processing through optimized data routing pathways. The system's performance is evaluated based on metrics such as data transmission latency, energy consumption, and overall network accuracy. This work represents a significant step towards creating more robust and efficient artificial intelligence systems, mimicking the adaptability found in the human brain. The key innovation lies in the integration of physical hardware with intelligent control mechanisms, resulting in a dynamic and responsive neural network architecture. The research focuses on establishing a foundational framework for adaptive neural networks that can handle evolving data landscapes with improved performance and reduced resource demands. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Dynamical Topology Optimization (DTO) provides a framework for optimizing complex network topologies by iteratively refining connections based on emergent properties. This paper introduces a novel algorithm, termed 'Adaptive Topology Evolution', which integrates reinforcement learning and graph theory to achieve automated design of complex networks, particularly in areas such as protein folding and neural networks. The core mechanism leverages a dynamically adjusted topology, guided by a reinforcement learning agent, to maximize functional efficiency and minimize energy dissipation, mimicking natural evolutionary processes. We demonstrate the effectiveness of the algorithm through illustrative examples, showcasing its ability to generate novel and optimized network structures with improved performance characteristics. The potential impact of this approach extends to diverse fields, including drug discovery and materials science, where intricate network designs are crucial for achieving desired functionalities.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the potential of neuromorphic reinforcement learning (RL) by integrating Spiking Temporal Memory (STM) networks. Traditional RL algorithms often struggle with complex, temporally extended tasks due to their reliance on explicit state representations and the challenges of handling noisy and asynchronous sensory data. STM, a biologically inspired neural network, excels at encoding and recalling temporal patterns directly from spiking neural activity. This work proposes an architecture where an RL agent is implemented using an STM network, allowing it to learn directly from raw sensory inputs, effectively capturing and utilizing the temporal dynamics inherent in the environment. The core claim is that this approach will lead to more efficient and robust RL by leveraging the temporal reasoning capabilities of STM. We outline the key components of the system, including the STM network architecture, the RL objective function, and the learning algorithm. The paper concludes with a discussion on the potential benefits and future research directions for this combined approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 maintenance through real-time feedback. We propose a decentralized control system where reinforcement learning algorithms continuously optimize control parameters – such as laser pulse shapes and magnetic field strengths – based on metrics like entanglement fidelity and coherence time. This adaptive strategy mitigates the challenges associated with traditional, fixed-parameter control schemes, which often struggle to account for the inherent noise and fluctuations in quantum systems. The resulting architecture represents a significant step towards truly autonomous quantum computing, promising enhanced scalability and robustness. This paper details the theoretical framework, the reinforcement learning algorithm employed, and outlines the potential impact of this approach on future quantum hardware development.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to neural network optimization centered around dynamically adapting the topology of the network through the adjustment of connections between neurons. The core idea is to create a neural network structure that is specifically tailored to a given task by leveraging reinforcement learning to govern the strength and pattern of connections. This adaptive topology allows for improved efficiency and generalization capabilities compared to traditional, static neural network architectures. The system learns to optimize the network's structure based on task feedback, resulting in a self-organizing network capable of adapting to varying demands. The key contribution lies in the integration of reinforcement learning with a dynamic topology, providing a framework for creating more efficient and robust neural networks. The theoretical framework presented here outlines a methodology for building adaptive neural networks, offering a promising direction for future research in neural network design and optimization.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Geometric Information Learning is a rapidly growing field with the potential to significantly enhance image recognition and object detection tasks. This paper explores the application of reinforcement learning to learn geometric information learning algorithms, aiming to improve the accuracy of these tasks. We propose a novel core mechanism that utilizes reinforcement learning to automatically adjust parameters, thereby boosting the learning efficiency and accuracy of the learned algorithms. This approach differs from existing methods, which typically require manual algorithm design. The research demonstrates that this new framework achieves superior performance compared to traditional methods, offering a promising path towards automated geometric information learning. The core claim is that utilizing reinforcement learning to learn geometric information learning algorithms can significantly improve the precision of image recognition and object detection.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to robot swarm coordination control utilizing Multi-Agent Reinforcement Learning (MARL). The core claim is to design a robust and adaptive robot swarm control system by leveraging the learning capabilities inherent in MARL. The proposed system employs a federated reinforcement learning architecture, where each robot is controlled by an individual agent. These agents learn through both information sharing and competitive interactions. The key innovation lies in replacing traditional rule-based or model-based control methods with a learning-based framework, enabling effective solutions to complex collaborative tasks in dynamic and uncertain environments. The system's design focuses on achieving efficient coordination and adaptability, ultimately leading to improved performance in challenging scenarios. This work provides a foundation for developing truly intelligent and self-organizing robot swarms. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
