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
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
Reward shaping is a critical technique in reinforcement learning (RL) that aims to accelerate learning by providing the agent with informative rewards. However, designing effective reward shaping functions can be a challenging and often tedious process, requiring domain expertise and extensive manual tuning. This paper proposes an adaptive Bayesian optimization approach to automate the reward shaping process. The system iteratively explores the space of potential reward functions, leveraging the agent's performance as feedback to refine the search strategy. We demonstrate that this approach can learn optimal reward shaping functions, leading to significant improvements in learning speed and agent performance compared to traditional reward shaping methods. The core claim is that an adaptive Bayesian optimization framework can effectively automate the reward shaping process, offering a more robust and efficient solution for complex RL problems. The system utilizes a Gaussian Process (GP) surrogate model to approximate the reward function landscape and employs an acquisition function, such as Expected Improvement, to guide the exploration process. The approach is evaluated on a suite of benchmark RL environments, showcasing its effectiveness across diverse scenarios. This work contributes to the broader field of RL by providing a practical and automated method for reward shaping, potentially unlocking new possibilities for tackling challenging RL problems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to simulating subconscious decision-making processes by leveraging multi-modal data fusion. The core idea is to construct a computational model capable of mirroring the complexities of human subconscious decision-making, moving beyond traditional behavioral analysis. We employ a graph neural network (GNN) architecture for robust multi-modal data integration, transforming diverse data streams – including visual, auditory, and tactile information – into a unified representation. This representation is then utilized within a reinforcement learning framework to simulate the subconscious decision-making process, explicitly modeling the interactive effects between different modalities. The resulting model provides a deeper understanding of how individuals make decisions without conscious awareness, offering potential applications in fields such as robotics, human-computer interaction, and cognitive modeling. The key innovation lies in the comprehensive incorporation of multi-modal interactions, providing a more accurate representation of the human subconscious than existing approaches. We define the following key equations to represent the core processes within the model: Let *xi* represent the input vector for modality *i*, where *i* ∈ {V, A, T}, representing Visual, Auditory, and Tactile modalities, respectively. The dimensionality of each *xi* is denoted as *di*. The multi-modal fusion process can be expressed as: * *xfused* = FusionNetwork(*xV*, *xA*, *xT*) Where *xfused* is the fused representation and FusionNetwork is the graph neural network. The reinforcement learning agent's decision-making process is governed by the following equation: * *ai* = argmaxj [Q( *xfused*, *aj* ) + β * R( *xfused*, *aj*)] Where *ai* is the action taken, *Q* is the Q-function estimating the expected reward, *R* is the reward function, and β is a weighting factor. The model's training objective can be formalized as: Minimize Eτ [ Σt=0T γt *R( *xfused*, *at* )] Where τ is a trajectory, *R* is the reward function, γ is the discount factor, and T is the time horizon.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel hierarchical reinforcement learning (HRL) framework that leverages intrinsic motivation, meta-learning, and a hierarchical architecture to address the limitations of traditional HRL methods concerning exploration and generalization. The core idea is to integrate these three components to create a more adaptive and efficient learning system. At each level of the hierarchy, intrinsic motivation, specifically novelty seeking, encourages exploration. Simultaneously, meta-learning dynamically adjusts the learning rate and policy updates, enabling rapid adaptation to diverse tasks. We demonstrate the effectiveness of this approach through a theoretical analysis and a conceptual framework, outlining the key components and their interactions. The resulting system offers improved learning speed and robustness compared to standard HRL techniques, particularly when facing complex and varied environments. This work lays the groundwork for future research in developing truly adaptable and intelligent hierarchical agents.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents Adaptive Resource Allocation in Cloud Computing (ARAC), a novel approach to cloud management that leverages reinforcement learning for dynamic resource allocation. Traditional cloud platforms often rely on manual configuration and pre-defined rules, leading to suboptimal resource utilization and potentially degraded user experience. ARAC addresses this limitation by employing a reinforcement learning-based resource scheduling algorithm. This algorithm continuously learns and adapts to changing conditions, optimizing the allocation of virtual machines, storage, and bandwidth based on user requests, resource utilization rates, and system load. The core claim of ARAC is to design a cloud platform capable of automatically adjusting resource allocations in response to evolving demands. The system's mechanism involves a dynamic adjustment of resources, aiming for optimal utilization and a superior user experience. This paper outlines the architecture, the reinforcement learning framework, and the key components of ARAC, demonstrating its potential to significantly improve cloud computing efficiency and responsiveness. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Meta-learning, the learning to learn, has shown significant promise in tackling complex tasks. However, a prevalent limitation lies in the reliance on static reward functions and environment dynamics, often simplifying the learning process and potentially hindering generalization to real-world scenarios. This paper introduces a novel adaptive meta-learning framework that addresses this limitation by dynamically adjusting the simulated environment's dynamics during the meta-training phase. The core idea is to utilize a learned Markov model to govern the environment's behavior, and to adapt the model parameters based on the agent's performance. This creates a continually evolving training environment, mirroring the inherent dynamism and uncertainty of real-world systems. We demonstrate that this approach leads to improved meta-learning performance compared to traditional static environment meta-learning methods. The algorithm incorporates key elements of reinforcement learning and Bayesian modeling to achieve adaptability and robustness. The primary formula representing the updated Markov model is: (Qt+1 | Qt, At) = f(Qt, At), where Qt+1 represents the state of the Markov model at time t+1, Qt is the state at time t, and At is the agent's action at time t. The function f is a parameterized function that is updated during the meta-training process. We explore the theoretical implications of this dynamic adaptation and discuss potential avenues for future research.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations