Integration Of Reinforcement Learning Research Articles

An innovative practical roadmap for optimal control strategies in malware propagation through the integration of RL with MPC

While there has been considerable research into optimal control formulations for mitigating cyber threats, a significant gap persists between the theoretical and numerical insights derived from such research and the practical implementation of these optimal mitigation strategies in real-time scenarios. This paper introduces a multifaceted approach to enhance and optimize optimal control strategies by seamlessly integrating reinforcement learning (RL) algorithms with model predictive control (MPC) techniques for the purpose of malware propagation control. Optimal control is a critical aspect of various domains, ranging from industrial processes and robotics to epidemiological modeling and cybersecurity. The traditional approaches to optimal control, particularly open-loop strategies, have limitations in adapting to dynamic and uncertain environments. This paper addresses these limitations by proposing a novel roadmap that leverages RL algorithms to fine-tune and adapt MPC parameters within the context of malware propagation containment. In sum, this practical roadmap is anticipated to serve as a valuable resource for researchers and practitioners engaged in the development of cybersecurity solutions.

Analysis of the Application of Artificial Intelligence in Mahjong

Over the past few years, AI-based models for Mahjong have received a substantial amount of contribution due to the advancements in machine learning and game theory. In this review, these state-of-the-art AI models are considered, including Tjong, Kanachan, Suphx, and Zhejiang University’s developed model. The deployed approaches are disparately represented by reinforcement learning, Monte Carlo tree search (MCTS), and deep neural networks, all of which aim at solving the issues that come with the game’s structure, such as incomplete and overburdening information. The Tjong model complements the power of the transformer architecture with hierarchical decision-making and in turn, brings strategic depth to the gameplay. Kanachan employs Q-learning and MCTS to optimize decision-making, while Suphx combines these methods with the novel ideas of Double Q-learning and Thompson Sampling to achieve higher performance than seen before. The integration of reinforcement learning and a new evaluation model imbues the model with the unique properties of both learning machines and human expertise, namely depth and intelligence. As a result of all these exploits, obstacles still exist, the most notable among them are the management of completely unknown information, handling long-distance games, and the strategic balance between offense and defense. In the future, determinants of probabilistic reasoning, the improvement of deep learning systems, and the prowess of reward shaping will result in AI improvement in Mahjong.

Design of an Improved Model for Multimodal Data Fusion Using XGBoost-LSTM-CNN and Proximal Policy Optimizations

Applications in healthcare, finance, and real-time sensor applications call for much stronger demands of improving accuracy and efficiency in the analysis of multimodal data. Current methods have deficiencies in fusing multiple types of data such as time-series, spatial, and categorical data, arising due to limitations in capturing sequential dependencies, spatial patterns, and feature importance simultaneously. It also discusses these challenges through a novel solution based on a hybrid ML-DL approach, with an integration of reinforcement learning and advanced probabilistic models. First, the method that comes to mind is the XGBoost-LSTM-CNN Hybrid Model, wherein three drivers of performance improvements come together, namely, XGBoost, with much-appreciated capability for outstanding handling of structured tabular data, LSTM due to its proficiency in capturing long-term temporal dependencies, and the strength of CNN in spatial feature extraction. These results improve the predictive accuracy for multimodal datasets. After that, further enhanced multimodal fusion by the CAMT selectively pays attention to the critical features across the modalities, enhancing the accuracy of the contextual time-series predictions. Subsequently, PPO enables real-time model adaptation by dynamic optimization of model parameters and improvement of predictions through continuous learning. KF-BNN reduces noise and uncertainty in time-series data by fusing the filtering capability of a Kalman filter with probabilistic modeling via Bayesian neural networks to give reliable predictions. It also provides federated learning via FedAvg with online gradient descent for distributed model training in a manner that ensures privacy, continuous model updates without having to centralize sensitive data samples. These approaches show significant improvements along many axes, with accuracy improvements of up to 4.5% and MAE reductions of 12% relative to the baseline models. Proposed models provide a valid framework for analyzing multimodal data, thereby increasing precision, recall, and overall adaptiveness in dynamic real time, hence pushing the state-of-the-art in multimodal fusion and timeseries prediction. This research probably will influence those areas dependent on distributed, multimodal, and sequential data samples.

Multi-Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm

Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban bus route optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, and accessibility while minimizing total travel distance. H-RL-VaNSAS is evaluated against leading optimization methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), and Salp Swarm Algorithm (SSA). Using metrics such as Hypervolume and the Average Ratio of Pareto Optimal Solutions, H-RL-VaNSAS demonstrates superior performance. Specifically, H-RL-VaNSAS achieved the highest resilience index (550), sustainability index (370), safety score (480), tourist preferences score (300), and accessibility score (2300), while minimizing total travel distance to 950 km. Compared to other methods, H-RL-VaNSAS improved resilience by 12.24–17.02%, sustainability by 5.71–12.12%, safety by 4.35–9.09%, tourist preferences by 7.14–13.21%, accessibility by 4.55–9.52%, and reduced travel distance by 9.52–17.39%. This research offers a framework for designing efficient, resilient, and sustainable public transit systems that align with urban planning and transportation goals. The integration of reinforcement learning with VaNSAS significantly enhances optimization capabilities, providing a valuable tool for mathematical and urban transportation research communities.

Integration of Reinforcement Learning and Deep Learning in Path Planning Games

This paper explores the dynamic intersection of Reinforcement Learning (RL) and Deep Learning (DL), emphasizing their collaborative strength in the realm of Deep Reinforcement Learning (DRL). The fusion of RL's strategy optimization through agent-environment interactions and DL's advanced perception and decision-making abilities results in DRL's powerful framework. This framework is extensively utilized to tackle intricate decision-making challenges across diverse fields. The paper begins by laying a solid theoretical foundation, explaining the core principles of both reinforcement learning and deep learning, and highlighting their synergistic potential. Further, the focus shifts to the practical implementation of the Deep Q Network (DQN) algorithm, particularly in the context of elementary path analysis problems. This segment starts with an accessible introduction to deep neural networks, paving the way for a deeper understanding of DQN. It then explores key aspects such as the utilization and accumulation of experiences, a critical component of the learning process in DRL. Additionally, the paper addresses the inherent limitations of the DQN algorithm, suggesting avenues for potential improvements and enhancements.

Advancements in Reinforcement Learning

Reinforcement Learning (RL) stands at the forefront of machine learning, offering a paradigm where agents learn to navigate complex environments through trial and error interactions. This paper provides a comprehensive overview of RL concepts, algorithms, challenges, and future directions. Key concepts such as agent-environment interaction, Markov Decision Processes (MDPs), value-based and policy-based methods are elucidated. Challenges including the exploration-exploitation trade-off, sample efficiency, and safety concerns are discussed, alongside potential breakthroughs in deep RL, handling continuous action spaces, and dealing with partial observability. The integration of RL with emerging technologies like IoT and its implications for real-world applications such as robotics, autonomous vehicles, and healthcare are explored. Finally, future trends and directions in RL research, including AI engineering and automated feature engineering, are outlined, highlighting the potential for transformative advancements and their impact on various domains. This paper serves as a comprehensive resource for researchers, practitioners, and enthusiasts interested in the evolving landscape of Reinforcement Learning

Research advanced in the integration of federated learning and reinforcement learning

Reinforcement learning (RL) and federated learning (FL) are two important machine learning paradigms. Reinforcement learning is concerned with enabling intelligence to learn optimal policies when interacting with an environment, while federated learning is concerned with collaboratively training models on distributed equipment while preserving data privacy. In recent years, the fusion and complementarity of reinforcement learning, and federated learning have attracted increasing research interest, providing new directions for the development of the machine learning community. Focusing on the integration of reinforcement learning and federated learning, this paper introduces in detail the latest technological developments in the integration of reinforcement learning and federated learning, and discusses the main challenges, existing methods and future directions of this intersection. Specifically, based on the introduction of classical reinforcement learning and federated learning. In addition, this document introduces cutting-edge results on the integration of reinforcement learning and joint learning and discusses the problems and future directions of the integration.

Application of integrating reinforcement learning and intelligent scheduling in logistics distribution

Logistics distribution is an indispensable part of the modern economy and is crucial for ensuring the efficient operation of the supply chain. With the continuous progress of technology, the application of intelligent scheduling systems in the field of logistics distribution is becoming increasingly widespread. Reinforcement learning, as one of the hot technologies in the field of artificial intelligence, is gradually receiving attention in its application in intelligent scheduling. Reinforcement learning can continuously learn and predict the same thing to enhance memory, while intelligent scheduling requires continuous prediction and optimization of logistics distribution paths. In response to the current problems of slow logistics distribution efficiency and low customer satisfaction, this article analyzed the application of intelligent scheduling in logistics distribution from the aspects of basic data maintenance, basic data review, intelligent scheduling, scheduling result review, distribution information management, and vehicle tracking. By using reinforcement learning, the traffic network weight in logistics distribution was studied to improve logistics distribution efficiency and customer satisfaction. This article analyzed the efficiency of logistics distribution, vehicle tracking accuracy, vehicle scheduling ability, and logistics distribution costs under different logistics distributions. The results showed that the logistics distribution under the integration of reinforcement learning and intelligent scheduling reduced 12.047 km compared to traditional distribution paths, and its distribution cost decreased by 129.718 yuan compared to traditional logistics distribution costs. The efficiency of logistics distribution that integrates reinforcement learning and intelligent scheduling has significantly improved, with optimized distribution costs and paths. It also has a positive effect on improving the utilization rate of logistics distribution vehicles.

Integration of Reinforcement Learning and Discrete Event Simulation

Integration of Reinforcement Learning and Discrete Event Simulation

Evolutionary Reinforcement Learning: A Survey

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impressive achievements in a wide range of challenging tasks, including board games, arcade games, and robot control. Despite these successes, several critical challenges remain, such as brittle convergence properties caused by sensitive hyperparameters, difficulties in temporal credit assignment with long time horizons and sparse rewards, a lack of diverse exploration, particularly in continuous search space scenarios, challenges in credit assignment in multi-agent RL, and conflicting objectives for rewards. Evolutionary computation (EC), which maintains a population of learning agents, has demonstrated promising performance in addressing these limitations. This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL). We categorize EvoRL methods according to key research areas in RL, including hyperparameter optimization, policy search, exploration, reward shaping, meta-RL, and multi-objective RL. We then discuss future research directions in terms of efficient methods, benchmarks, and scalable platforms. This survey serves as a resource for researchers and practitioners interested in the field of EvoRL, highlighting the important challenges and opportunities for future research. With the help of this survey, researchers and practitioners can develop more efficient methods and tailored benchmarks for EvoRL, further advancing this promising cross-disciplinary research field.

Survey of imitation learning: tradition and new advances

模仿学习是强化学习与监督学习的结合，目标是通过观察专家演示，学习专家策略，从而加速强化学习。通过引入任务相关的额外信息，模仿学习相较于强化学习，可以更快地实现策略优化，为缓解低样本效率问题提供了解决方案。模仿学习已成为解决强化学习问题的一种流行框架，涌现出多种提高学习性能的算法和技术。通过与图形图像学的最新研究成果相结合，模仿学习已经在游戏人工智能（artificial intelligence，AI）、机器人控制和自动驾驶等领域发挥了重要作用。本文围绕模仿学习的年度发展，从行为克隆、逆强化学习、对抗式模仿学习、基于观察量的模仿学习和跨领域模仿学习等多个角度进行深入探讨，介绍了模仿学习在实际应用上的最新情况，比较了国内外研究现状，并展望了该领域未来的发展方向。旨在为研究人员和从业人员提供模仿学习的最新进展，从而为开展工作提供参考与便利。;Imitation learning(IL) is focused on the integration of reinforcement learning and supervised learning through observing demonstrations and learning expert strategies. The additional information related imitation learning can be used to optimize and implement its strategy, which can provide the possibility to alleviate low efficiency of sample problem. In recent years, imitation learning has become a popular framework for solving reinforcement learning problems, and a variety of algorithms and techniques have emerged to improve the performance of learning procedure. Combined with the latest research in the field of image processing, imitation learning has played an important role in such domains like game artificial intelligence(AI), robot control, autonomous driving. Traditional imitation learning methods are mainly composed of behavioral cloning(BC), inverse reinforcement learning(IRL), and adversarial imitation learning(AIL). Thanks to the computing ability and upstream graphics and image tasks(such as object recognition and scene understanding), imitation learning methods can be used to integrate a variety of technologies-emerged for complex tasks. We summarize and analyze imitation learning further, which is composed of imitation learning from observation(ILfO) and cross-domain imitation learning(CDIL). The ILfO can be used to optimize the requirements for expert demonstration, and information-observable can be learnt only without specific action information from experts. This setting makes imitation learning algorithms more practical, and it can be applied to real-life scenes. To alter the environment transition dynamics modeling, ILfO algorithms can be divided into two categories:model-based and model-free. For model-based methods, due to path-constructed of the model in the process of interaction between the agent and the environment, it can be assorted into forward dynamic model and inverse dynamic model further. The other related model-free methods are mainly composed of adversarial-based and function-rewarded engineering. Cross-domain imitation learning are mainly focused on the status of different domains for agents and experts, such as multiple Markov decision processes. Current CDIL research are mainly focused on the domain differences of three aspects of discrepancy in relevant to:transition dynamics, morphological, and view point. The technical solutions to CDIL problems can be mainly divided into such methods like:direct, mapping, adversarial, and optimal transport. The application of imitation learning is mainly on such aspects like game AI, robot control, and automatic driving. The recognition and perception capabilities of intelligent agents are optimized further in image processing, such as object detection, video understanding, video classification, and video recognition. Our critical analysis can be focused on the annual development of imitation learning from the five aspects:behavioral cloning, inverse reinforcement learning, adversarial imitation learning, imitation learning from observation, and cross-domain imitation learning.

A surrogate-assisted controller for expensive evolutionary reinforcement learning

The integration of Reinforcement Learning (RL) and Evolutionary Algorithms (EAs) aims at simultaneously exploiting the sample efficiency as well as the diversity and robustness of the two paradigms. Recently, hybrid learning frameworks based on this principle have achieved great success in robot control tasks. However, in these methods, policies from the genetic population are evaluated via interactions with the real environments, severely restricting their applicability when such interactions are prohibitively costly. In this work, we propose Surrogate-assisted Controller (SC), a generic module that can be applied on top of existing hybrid frameworks to alleviate the computational burden of expensive fitness evaluation. The key to our approach is to leverage the critic network that is implemented in existing hybrid frameworks as a novel surrogate model, making it possible to estimate the fitness of individuals without environmental interactions. In addition, two model management strategies with the elite protection mechanism are introduced in SC to control the workflow, leading to a fast and stable optimization process. In the empirical studies, we combine SC with two state-of-the-art evolutionary reinforcement learning approaches to highlight its functionality and effectiveness. Experiments on six challenging continuous control benchmarks from the OpenAI Gym platform show that SC can not only significantly reduce the cost of interaction with the environment, but also bring better sample efficiency and dramatically boost the learning progress of the original hybrid framework.

A Mini review on Effectiveness of Reinforcement learning in Stroke Rehabilitation

This review focuses on the Development of Reinforcement based therapies in the field of Rehabilitation of Post -Stroke Patients. The process through which a system's (or organism's) reaction to a stimulus is increased by reward and diminished by punishment (Positive and Negative Reinforcement, respectively) is referred to as reinforcement learning. [1] A plausible set of methods for recreating this reward distribution from experience has been discovered following recent breakthroughs in machine learning. Integration of Reinforcement learning through a Physiotherapeutic perspective is presented in this review. The Brain-Machine interface is a relatively newer as well as potential field which can be used for stroke rehabilitation based on the principles of reinforcement learning which is a subset of Machine Learning.

Q-Learning-Based Teaching-Learning Optimization for Distributed Two-Stage Hybrid Flow Shop Scheduling with Fuzzy Processing Time

Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings. However, the distributed two-stage hybrid flow shop scheduling problem (DTHFSP) with fuzzy processing time is seldom investigated in multiple factories. Furthermore, the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP. In the current study, DTHFSP with fuzzy processing time was investigated, and a novel Q-learning-based teaching-learning based optimization (QTLBO) was constructed to minimize makespan. Several teachers were recruited for this study. The teacher phase, learner phase, teacher's self-learning phase, and learner's self-learning phase were designed. The Q-learning algorithm was implemented by 9 states, 4 actions defined as combinations of the above phases, a reward, and an adaptive action selection, which were applied to dynamically adjust the algorithm structure. A number of experiments were conducted. The computational results demonstrate that the new strategies of QTLBO are effective; furthermore, it presents promising results on the considered DTHFSP.

Integration of Reinforcement Learning in a Virtual Robotic Surgical Simulation.

Background. The revolutions in AI hold tremendous capacity to augment human achievements in surgery, but robust integration of deep learning algorithms with high-fidelity surgical simulation remains a challenge. We present a novel application of reinforcement learning (RL) for automating surgical maneuvers in a graphical simulation.Methods. In the Unity3D game engine, the Machine Learning-Agents package was integrated with the NVIDIA FleX particle simulator for developing autonomously behaving RL-trained scissors. Proximal Policy Optimization (PPO) was used to reward movements and desired behavior such as movement along desired trajectory and optimized cutting maneuvers along the deformable tissue-like object. Constant and proportional reward functions were tested, and TensorFlow analytics was used to informed hyperparameter tuning and evaluate performance.Results. RL-trained scissors reliably manipulated the rendered tissue that was simulated with soft-tissue properties. A desirable trajectory of the autonomously behaving scissors was achieved along 1 axis. Proportional rewards performed better compared to constant rewards. Cumulative reward and PPO metrics did not consistently improve across RL-trained scissors in the setting for movement across 2 axes (horizontal and depth).Conclusion. Game engines hold promising potential for the design and implementation of RL-based solutions to simulated surgical subtasks. Task completion was sufficiently achieved in one-dimensional movement in simulations with and without tissue-rendering. Further work is needed to optimize network architecture and parameter tuning for increasing complexity.

Integration of reinforcement learning and model predictive control to optimize semi‐batch bioreactor

AbstractAs the digital transformation of the bioprocess is progressing, several studies propose to apply data‐based methods to obtain a substrate feeding strategy that minimizes the operating cost of a semi‐batch bioreactor. However, the negligent application of model‐free reinforcement learning (RL) has a high chance to fail on improving the existing control policy because the available amount of data is limited. In this article, we propose an integrated algorithm of double‐deep Q‐network and model predictive control. The proposed method learns the action‐value function in an off‐policy fashion and solves the model‐based optimal control problem where the terminal cost is assigned by the action‐value function. For simulation study, the proposed method, model‐based method, and model‐free methods are applied to the industrial scale penicillin process. The results show that the proposed method outperforms other methods, and it can learn with fewer data than model‐free RL algorithms.

An adaptive artificial bee colony with reinforcement learning for distributed three-stage assembly scheduling with maintenance

Distributed three-stage assembly scheduling problem extensively exists in the real-life assembly production process and is seldom considered. The integration of reinforcement learning with meta-heuristic can effectively improve the performance of meta-heuristic and effectively solve the problem; however, the integration is seldom used to cope with the problem. In this study, distributed three-stage assembly scheduling problem with DPm→1 layout and maintenance at three stages is considered and a mathematical model is provided. A new artificial bee colony with Q-learning (QABC) is proposed to minimize maximum tardiness. An effective Q-learning algorithm is implemented to dynamically select search operator, which consists of 12 states based on population quality evaluation, 8 actions defined by global search and neighborhood search, a new reward and an effective action selection. Two employed bee swarms are formed, an adaptive communication and an adaptive competition process between them are adopted to intensify exploration ability and improve search efficiency. QABC and its four comparative algorithms are tested on 80 instances. The computational results demonstrate that the new strategies of QABC really improve its search performance and QABC is a competitive algorithm for the considered problem.

Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model

The integration of reinforcement learning (RL) and imitation learning (IL) is an important problem that has long been studied in the field of intelligent robotics. RL optimizes policies to maximize the cumulative reward, whereas IL attempts to extract general knowledge about the trajectories demonstrated by experts, i.e, demonstrators. Because each has its own drawbacks, many methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of these methods are heuristic and do not have a solid theoretical basis. This paper presents a new theory for integrating RL and IL by extending the probabilistic graphical model (PGM) framework for RL, control as inference. We develop a new PGM for RL with multiple types of rewards, called probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of RL and IL can be formulated as a probabilistic inference of policies on pMDP-MO by considering the discriminator in generative adversarial imitation learning (GAIL) as an additional optimality emission. We adapt the GAIL and task-achievement reward to our proposed framework, achieving significantly better performance than policies trained with baseline methods.

A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition.

On the issues of global environment protection, the renewable energy systems have been widely considered. The photovoltaic (PV) system converts solar power into electricity and significantly reduces the consumption of fossil fuels from environment pollution. Besides introducing new materials for the solar cells to improve the energy conversion efficiency, the maximum power point tracking (MPPT) algorithms have been developed to ensure the efficient operation of PV systems at the maximum power point (MPP) under various weather conditions. The integration of reinforcement learning and deep learning, named deep reinforcement learning (DRL), is proposed in this paper as a future tool to deal with the optimization control problems. Following the success of deep reinforcement learning (DRL) in several fields, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) are proposed to harvest the MPP in PV systems, especially under a partial shading condition (PSC). Different from the reinforcement learning (RL)-based method, which is only operated with discrete state and action spaces, the methods adopted in this paper are used to deal with continuous state spaces. In this study, DQN solves the problem with discrete action spaces, while DDPG handles the continuous action spaces. The proposed methods are simulated in MATLAB/Simulink for feasibility analysis. Further tests under various input conditions with comparisons to the classical Perturb and observe (P&O) MPPT method are carried out for validation. Based on the simulation results in this study, the performance of the proposed methods is outstanding and efficient, showing its potential for further applications.

A Digital Receding-Horizon Learning Controller for Nonlinear Continuous-time Systems

Abstract The integration of reinforcement learning (RL) and model predictive control (MPC) is promising for solving nonlinear optimization problems in an efficient manner. In this paper, a digital receding horizon learning controller is proposed for continuous-time nonlinear systems with control constraints. The main idea is to develop a digital design for RL with actor-critic design (ACD) in the framework of MPC, to realize near-optimal control of continuous-time nonlinear systems. Different from classic RL for continuous-time systems, the actor adopted is learned in discrete-time steps, while the critic evaluates the learned control policy continuously in the time domain. Moreover, we use soft barrier functions to deal with control constraints and the robustness of the actor-critic network is proven. A simulation example is considered to show the effectiveness of the proposed approach.