Deep Reinforcement Learning (DRL) has demonstrated remarkable success in solving complex control problems. However, deploying DRL agents in real-world scenarios, particularly in safety-critical applications, presents significant challenges. The inherent stochasticity and complex decision-making processes of DRL policies often lead to unpredictable and potentially unsafe behavior. This work addresses this challenge by formally verifying the safety and correctness of DRL policies using temporal logic. We construct a temporal logic model that precisely captures the desired behavior of the policy and employ model checking techniques to rigorously determine whether the policy satisfies the specified temporal logic constraints. This approach offers a novel and rigorous method for ensuring the reliability and trustworthiness of DRL systems, bridging the gap between the empirical successes of DRL and the formal guarantees provided by verification techniques. The core contribution lies in applying temporal logic as a precise specification language to define the desired behavior of a DRL policy, enabling automated verification of its adherence to safety and correctness properties. This process moves beyond simply observing policy performance to providing a mathematically sound basis for confidence in the policy's operation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The efficient scheduling of distributed machine learning (ML) jobs presents a significant challenge due to the complex interplay of factors such as varying job resource requirements, heterogeneous computing environments, and dynamic workload fluctuations. Traditional scheduling approaches often rely on heuristics or simple optimization techniques, which may not effectively address the inherent complexities of ML workflows. This paper proposes a novel approach leveraging Markov Decision Processes (MDPs) to model and solve this scheduling problem. We formulate an MDP where states represent the current job queue and resource availability, and actions represent scheduling decisions, such as assigning a job to a specific worker or delaying its execution. A reinforcement learning (RL) algorithm is then employed to learn an optimal scheduling policy through interaction with the MDP. This approach offers a more principled and potentially more effective solution compared to traditional methods, leading to improved resource utilization, reduced job completion times, and overall enhanced performance of distributed ML systems. The core contribution lies in the formalization of the scheduling problem within an MDP framework and the subsequent application of RL to discover optimal scheduling strategies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel recursive learning system (BRRLS) that leverages biofeedback to enable adaptive learning. The core idea is to utilize real-time monitoring of physiological signals, such as heart rate and electroencephalography (EEG), to construct a system capable of dynamically adjusting its learning strategy. The system employs reinforcement learning algorithms, where the biofeedback signals serve as both learning objectives and feedback signals. This allows for the continuous optimization of learning parameters and strategies, leading to improved performance on complex tasks. The novelty of BRLS lies in its direct integration of biological feedback mechanisms to imbue the learning system with self-regulatory capabilities, mirroring biological learning processes. This approach has significant potential applications in areas such as robotic control and human-computer interaction. The system's architecture incorporates a feedback loop designed for iterative improvement, fundamentally distinguishing it from traditional, static learning models. Mathematical formulations detail the key components and operational principles of the BRRLS, emphasizing the role of state estimation, reward function design, and policy optimization within the reinforcement learning framework. The system is designed for modularity, allowing for the integration of diverse biofeedback modalities and reinforcement learning algorithms. Future research will focus on scaling the system to handle more complex tasks and exploring the potential for transferring learned strategies to different environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel hybrid framework integrating the Simulated Evolution Algorithm (SEA) and Deep Reinforcement Learning (DRL) to tackle complex optimization problems. The core idea is to leverage SEA's global search capability for initial exploration and DRL's local optimization prowess for refining solutions. The framework operates through an iterative process: SEA generates a diverse population of potential solutions, and DRL is then employed to optimize individual solutions or subsets of the population. Crucially, the parameters of both algorithms are iteratively updated based on their performance, enabling a synergistic evolution. We demonstrate the framework's effectiveness through theoretical analysis and a detailed explanation of the mechanisms involved. The key contribution lies in establishing a robust and adaptable method for combining these two powerful techniques, promising improved efficiency and solution quality compared to using them independently. This work provides a foundational approach for future research exploring the synergy between evolutionary and reinforcement learning methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to program understanding and optimization, termed Dynamic Semantic Topology Learning and Program Reasoning. The core claim is that program behavior is not solely determined by its source code, but significantly influenced by the dynamic semantic relationship network within its runtime environment, which evolves during execution. We propose a mechanism utilizing Graph Neural Networks (GNNs) to construct and continuously update a program semantic topology. Nodes in this topology represent program elements such as functions, variables, and data structures, while edges represent their semantic dependencies – including call relationships, data flow relationships, and control flow relationships. A reinforcement learning-based GNN learning algorithm leverages feedback signals from program execution (e.g., results, errors, resource consumption) to adjust the topology, enhancing its accuracy in reflecting program behavior. This dynamic approach overcomes limitations of static semantic representations like Abstract Syntax Trees (ASTs) and Control Flow Graphs (CFGs) by capturing the complex, runtime-dependent relationships, ultimately leading to improved program understanding and optimization. The methodology offers a new paradigm for analyzing and reasoning about program logic, particularly in dynamic and complex systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of probabilistic programming to the field of model-based reinforcement learning (MBRL). Traditional MBRL approaches often rely on deterministic models, which can be brittle and fail to adequately represent the inherent uncertainty in real-world environments. We argue that leveraging probabilistic programming languages allows for the creation of more robust, interpretable, and adaptable RL agents. The core concept involves representing both the environment dynamics and the agent's policy as probabilistic models. This enables the agent to explicitly reason about uncertainty, quantify its confidence in predictions, and ultimately, make more informed decisions. We demonstrate the potential of this approach through a theoretical framework, focusing on the formulation of probabilistic models for state transition and reward functions. The resulting agent can dynamically update its understanding of the environment, leading to improved performance and increased resilience to unforeseen circumstances. This work provides a foundation for future research in probabilistic MBRL and highlights the importance of incorporating uncertainty into the design of intelligent agents.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the development of an algorithmic chaos theory system designed to generate complex, multi-dimensional chaos patterns without relying on predefined, fixed parameters. Leveraging a reinforcement learning-inspired approach, the algorithm continuously adjusts its internal parameters based on observed system behavior, fostering emergent chaos generation. We propose a novel framework that moves beyond traditional chaotic systems, emphasizing dynamic adaptation and self-governing behavior, ultimately aiming to create systems exhibiting a higher degree of unpredictability. The core mechanism centers around a feedback loop where the algorithm's output is evaluated, and adjustments are made to its internal state, driving the creation of intricate chaotic configurations. This research addresses a key challenge in chaos theory – the requirement for adaptive systems – and offers a new methodology for exploring and generating complex dynamic systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the potential of quantum computing to revolutionize Monte Carlo Tree Search (MCTS) algorithms, a cornerstone technique in reinforcement learning and game AI. The core claim is that by harnessing quantum superposition and interference, we can significantly accelerate MCTS's exploration of complex optimization landscapes. The proposed approach utilizes quantum circuits to represent and evaluate game states, leveraging quantum parallelism to concurrently assess multiple branches of the search tree. We demonstrate, through theoretical analysis and algorithmic design, how this quantum-enhanced MCTS can outperform classical MCTS in scenarios with high computational complexity and vast search spaces. The resulting system offers a novel approach to solving complex optimization problems, particularly those found in areas such as game playing, portfolio optimization, and drug discovery. The presented methodology focuses on the conceptual framework and provides a roadmap for future research and development, emphasizing the integration of quantum hardware with sophisticated reinforcement learning strategies. The key innovation lies in the efficient mapping of the MCTS search process onto a quantum computing architecture, exploiting quantum mechanics to dramatically reduce the search time.
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
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