This paper presents a novel reinforcement learning (RL) algorithm, Dynamic Kernel Learning (DKL), designed to enhance the performance of RL agents in complex environments. DKL addresses the limitations of traditional RL methods that often rely on fixed kernel functions for value estimation. The core innovation lies in the continuous adaptation of the kernel function itself through a meta-learning approach. A dedicated neural network learns to adjust the kernel parameters, such as the bandwidth of a Gaussian kernel, based on the agent's reward signals and state transitions. This dynamic adaptation allows the agent to effectively generalize across diverse states and improve exploration efficiency. We demonstrate the effectiveness of DKL through theoretical analysis and outline its key components and training procedure. The algorithm offers a promising direction for improving the robustness and adaptability of RL agents, particularly in scenarios with high-dimensional state spaces and non-stationary environments. The key benefit is the ability to tailor the value function representation to the current state of the environment, leading to faster convergence and better final performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel reinforcement learning (RL) algorithm, Dynamic Kernel Learning (DKL), designed to enhance the performance of RL agents in complex environments. DKL addresses the limitations of traditional RL methods that often rely on fixed kernel functions for value estimation. The core innovation lies in the continuous adaptation of the kernel function itself through a meta-learning approach. A dedicated neural network learns to adjust the kernel parameters, such as the bandwidth of a Gaussian kernel, based on the agent's reward signals and state transitions. This dynamic adaptation allows the agent to effectively generalize across diverse states and improve exploration efficiency. We demonstrate the effectiveness of DKL through theoretical analysis and outline its key components and training procedure. The algorithm offers a promising direction for improving the robustness and adaptability of RL agents, particularly in scenarios with high-dimensional state spaces and non-stationary environments. The key benefit is the ability to tailor the value function representation to the current state of the environment, leading to faster convergence and better final performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Multi-Agent Reinforcement Learning (MARL) offers a promising approach to tackling complex cooperative tasks. However, existing MARL algorithms often fail to achieve robust coordination and cooperation, primarily due to difficulties in learning effective joint policies and the lack of mechanisms to encourage collaborative behavior. This paper proposes a novel framework that integrates intrinsic motivation and cooperative reward shaping into a MARL system. The core idea is to augment the traditional extrinsic reward signal with internal drives, such as curiosity and competence, and to shape the reward function to explicitly incentivize cooperation among agents. We introduce a framework where agents learn to maximize both their external rewards and their internal motivation levels, while simultaneously benefiting from a carefully designed cooperative reward structure. The theoretical analysis demonstrates the potential of this approach to overcome the limitations of standard MARL and to promote more efficient and stable cooperative learning. We present a detailed description of the framework and discuss its key components, highlighting the interplay between intrinsic motivation, cooperative reward shaping, and the overall learning process. The results, although presented without empirical experimentation, illustrate the potential impact of this approach on improving cooperative MARL performance. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Reinforcement learning (RL) faces significant challenges in achieving optimal performance due to the inherent difficulty in balancing exploration and exploitation. This paper proposes a novel framework leveraging probabilistic programming (PP) to address this problem. The core idea is to represent an RL agent as a probabilistic program, enabling the explicit modeling of uncertainty within the environment and the agent itself. This allows for the incorporation of Bayesian approaches to exploration, where actions are actively sampled based on predicted rewards and a quantified measure of uncertainty. Unlike traditional RL methods that often rely on heuristics or point-estimate models, this approach provides a more principled and flexible framework for exploration, potentially leading to improved sample efficiency and overall performance, particularly in complex and partially observable environments. The framework utilizes concepts from Bayesian inference, Markov Decision Processes (MDPs), and probabilistic programming to define a coherent and powerful approach to RL. Key elements include the definition of probability distributions for state values, action values, and transition probabilities, and the application of variational inference or other inference techniques to estimate these distributions. This allows the agent to learn not just optimal actions, but also a representation of its knowledge about the environment.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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This paper proposes a novel approach to reinforcement learning (RL) that leverages relational reasoning through the incorporation of graph-based reward shaping. Traditional RL algorithms often struggle in environments exhibiting complex relationships between entities, leading to inefficient learning and suboptimal policies. Our method addresses this limitation by enabling the agent to explicitly learn and represent these relationships, utilizing a graph structure to encode contextual information. The core idea is to shape the reward function based on the agent's understanding of these relationships, guiding it towards more effective exploration and exploitation. This approach is demonstrated through a theoretical framework outlining the algorithm and key formulas, along with a conceptual explanation of its implementation. The potential for enhanced learning efficiency and policy quality in environments with inherent relational complexity is highlighted. The algorithm's performance is expected to improve by considering the interactions between entities within an environment, rather than solely focusing on individual state-action pairs.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Enterprise platforms are evolving from systems that primarily record and analyze business operations into intelligent environments capable of predicting outcomes, recommending interventions, executing decisions, and learning from their consequences. This article proposes a conceptual Autonomous Enterprise Platform (AEP) based on continuous AI-guided decision loops integrating enterprise sensing, contextual intelligence, predictive analytics, decision intelligence, prescriptive policies, autonomous execution, learning, and governance. The proposed framework extends the classical Monitor, Analyze, Plan, and Execute model of autonomic computing by incorporating continuous prediction, intervention, evaluation, and adaptation. The study synthesizes research published between 2000 and 2022 on autonomous agents, autonomic computing, self-adaptive systems, predictive process monitoring, reinforcement learning, and prescriptive analytics. Three key studies provide the conceptual foundation: Kephart and Chess on autonomic computing, Metzger et al. on proactive process adaptation using deep learning, and Kubrak et al. on prescriptive process monitoring. The framework distinguishes operational, learning, and governance loops to support continuous enterprise adaptation. It emphasizes the transition from predicting business outcomes to selecting and executing appropriate interventions. The study also examines challenges involving causal reasoning, intervention timing, resource constraints, model drift, explainability, and human oversight. Overall, AI-guided decision loops provide a foundation for adaptive, intelligent, and governed enterprise platforms capable of continuous decision making, organizational learning, and operational optimization.
Shekar Vollem· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to artificial intelligence—Neuro-Symbolic Logic Programming with Reinforcement Learning—designed to address the limitations of current AI techniques. The core idea is to integrate the pattern recognition capabilities of neural networks with the reasoning and explainability offered by symbolic logic programming, guided by reinforcement learning. We present a hybrid system where a neural network learns a high-level representation of a task, translating sensory inputs into abstract concepts. This representation is then fed into a symbolic logic engine, which executes predefined rules and generates plans. Reinforcement learning is utilized to optimize the neural network's representation and the logic engine's rule selection, allowing the system to adapt and improve its performance over time. This approach aims to create AI systems that are not only capable of complex behavior but also provide verifiable, logically sound explanations for their actions. The system's architecture and the interaction between its components are detailed, highlighting the potential for robust and explainable AI. We demonstrate a conceptual framework, outlining the key components and their interplay, and discuss potential future research directions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to system optimization leveraging Multi-Agent Reinforcement Learning (MARL). The core concept is to decompose complex systems into multiple intelligent agents, each responsible for controlling a specific subsystem. These agents then collaboratively learn optimal control strategies through a MARL framework. Traditional system optimization methods often rely heavily on expert knowledge and manual tuning, which can be time-consuming and limited in scope. MARSL offers a dynamic and adaptive solution, allowing systems to learn and optimize themselves based on real-time feedback and interactions. This approach is particularly well-suited for systems with high dimensionality, non-linear dynamics, and complex interactions between components. The paper outlines the theoretical foundations of MARSL, details the implementation considerations, and presents a conceptual framework for its application. We aim to demonstrate that MARL can significantly enhance the optimization capabilities of complex systems compared to conventional methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to dynamic program generation, termed "Based on Multi-Modal Context for Dynamic Program Generation," which moves beyond static template-based code modification by leveraging a combination of code, natural language descriptions, execution logs, and user interaction data. The core of this system is a reinforcement learning (RL) model that dynamically adapts and generates code in response to multi-modal input. The model learns to generate code that aligns with both the specified requirements and the observed behavior of the generated code during execution, guided by user feedback. We demonstrate that this multi-modal approach significantly improves the flexibility and adaptability of program generation compared to traditional methods that rely on single modality inputs or static templates. The key innovation lies in the model's ability to learn a continuous representation of the desired program state, enabling it to handle complex and dynamic programming scenarios. We outline the architecture, training methodology, and initial experimental results, showcasing the potential of this approach for automating software development. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the integration of chaos dynamic optimization algorithms with reinforcement learning to develop a robust and adaptable system for complex systems, particularly within control and design domains. Traditional optimization methods often rely on handcrafted parameters, limiting flexibility. This research proposes a novel approach that leverages reinforcement learning to dynamically adjust algorithm parameters, fostering a system that autonomously learns and optimizes behavior. The core mechanism centers around employing reinforcement learning to refine the chaos dynamic optimization process, resulting in enhanced accuracy and adaptability. We demonstrate the effectiveness of this fusion through simulations and a preliminary case study involving a dynamic control system. This work establishes a foundation for intelligent system design and offers a promising path towards more flexible and autonomous optimization strategies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to distributed system management called Dynamic Topo-Semantic Network Learning (DTSNL). DTSNL leverages reinforcement learning to enable systems to automatically discover and adapt to changes in underlying hardware and software topology. The core idea is to deploy agents, each responsible for a specific network node or resource, which learn both task execution and topological awareness. These agents utilize sensor data, logs, and monitoring information to observe and understand the network topology. The learning objective is to minimize communication latency, maximize resource utilization, and dynamically adjust routing and communication protocols to accommodate topological changes such as node failures, network congestion, or new node additions. A key component is a "topology-aware" reward function that incentivizes agents to learn sensitivity and adaptability to these changes. DTSNL represents a significant advancement over existing network learning methods that typically assume static topologies, offering a robust and self-optimizing solution for complex and dynamic network environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to reinforcement learning (RL) that leverages relational reasoning through the incorporation of graph-based reward shaping. Traditional RL algorithms often struggle in environments exhibiting complex relationships between entities, leading to inefficient learning and suboptimal policies. Our method addresses this limitation by enabling the agent to explicitly learn and represent these relationships, utilizing a graph structure to encode contextual information. The core idea is to shape the reward function based on the agent's understanding of these relationships, guiding it towards more effective exploration and exploitation. This approach is demonstrated through a theoretical framework outlining the algorithm and key formulas, along with a conceptual explanation of its implementation. The potential for enhanced learning efficiency and policy quality in environments with inherent relational complexity is highlighted. The algorithm's performance is expected to improve by considering the interactions between entities within an environment, rather than solely focusing on individual state-action pairs.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
