Multi-armed Bandit Algorithm Research Articles

Comparative Evaluation, Challenges, and Diverse Applications of Multi-Armed Bandit Algorithms

This manuscript offers an exhaustive comparative study of key Multi-Armed Bandits (MAB) algorithms, exploring their challenges, potential resolutions, and varied applications in today's context. It concentrates on at least three principal algorithms: the Upper Confidence Bound (UCB), Thompson Sampling, and ε-greedy. The analysis critically assesses their performance, focusing on convergence rate, precision, and computational demands. Key challenges in non-static environments and large-scale deployments are identified, including the complexities of multi-objective optimization. The paper proposes innovative solutions such as adaptive algorithmic approaches and the integration of parallel computing frameworks to address these challenges. It further delves into a range of application domains, from online advertising and recommendation systems to clinical trial methodologies, drawing comparisons between traditional and novel applications. The discussion also encompasses critical issues like data scarcity, cold start problems, ethical considerations in algorithm design, and the intricacies of processing real-time data. In its concluding sections, the study sheds light on recent successful deployments of MAB algorithms, identifying core factors contributing to their effectiveness and forecasting future developmental trajectories. This comprehensive analysis provides a detailed overview of the MAB field, highlighting its significance and practical impact in various sectors.

In-depth Exploration and Implementation of Multi-Armed Bandit Models Across Diverse Fields

This paper presents an in-depth analysis of the Multi-Armed Bandit (MAB) problem, tracing its evolution from its origins in the gambling domain of the 1940s to its current prominence in machine learning and artificial intelligence. The analysis begins with a historical overview, noting key developments like Herbert Robbins' probabilistic framework and the expansion of the problem into strategic decision-making in the 1970s. The emergence of algorithms like the Upper Confidence Bound (UCB) and Thompson Sampling in the late 20th century is highlighted, demonstrating the MAB problem's transition to practical applications. The integration of MAB algorithms with machine learning, particularly in the era of reinforcement learning, is explored, emphasizing their application in various domains such as online advertising, financial market trading, and clinical trials. The paper discusses the critical role of decision theory and probabilistic models in MAB problems, focusing on the balance between exploration and exploitation strategies. Recent advancements in Contextual Bandits, non-stationary reward distributions, and Multi-agent Bandits are examined, showcasing the ongoing evolution and adaptability of MAB problems.

Resource Allocation for LoRaWAN Network Slicing: Multi-Armed Bandit-based Approaches

Wireless sensor networks have become increasingly popular in recent years due to the growing demand for Internet of Things (IoT) applications, including LoRaWAN networks. However, the effective allocation of resources remains a crucial challenge in LoRaWAN networks due to the limited bandwidth and the diverse demands for multiple services. This paper presents three novel resource allocation solutions for LoRaWAN network slicing to address this challenge. These solutions are based on the Multi-Armed Bandit (MAB) algorithm, which is known for balancing the exploration of available actions with the exploitation of optimal decisions. Our objective is to dynamically and efficiently allocate resources to network slices by treating the resource allocation as a MAB problem. This approach aims to maximize Packet Delivery Rate (PDR) performance while ensuring each service’s Service Level Agreement (SLA). The first solution, UCB-MAB, uses the Upper Confidence Bound (UCB) strategy to balance exploration and exploitation to improve network performance. The second solution, Q-UCB-MAB, continuously updates Q-values using the Q-learning update equation and incorporates the UCB strategy for further optimization. Finally, the third solution, ARIMA-UCB-MAB, leverages the predicted reward value from the Autoregressive Integrated Moving Average (ARIMA) model within the UCB framework to enhance network performance. Our results demonstrate that all three solutions offer efficient resource allocation in terms of PDR and SLA satisfaction. Specifically, the ARIMA-UCB-MAB solution outperforms the two other solutions.

An efficient beaconing of bluetooth low energy by decision making algorithm

Ongoing research endeavors are exploring the potential of artificial intelligence to enhance the efficiency of wireless communication systems. Nevertheless, complex computational mechanisms, such as those inherent in neural networks, are not optimally suited for applications where the reduction of computational intricacy is of paramount importance. The rise in Bluetooth-enabled devices has led to the widespread adoption of Bluetooth Low Energy (BLE) in various IoT applications, primarily due to its low power consumption. For specific applications, such as lost and found tags which operate on small batteries, it’s especially important to further reduce power usage. With the objective of achieving low power consumption by optimally selecting channels and advertisement intervals, this paper introduces a parameter selection method derived from the Multi-Armed Bandit (MAB) algorithm, a technique known for addressing human decision-making challenges. In this study, we evaluate our proposed method using simulations in diverse environments. The outcomes indicate that, without compromising much on reliability, our approach can reduce power consumption by up to 40% based on the wireless surroundings. Additionally, when this method was implemented on an actual BLE device, it demonstrated effectiveness in reducing power consumption by about 35% in real environments.

Assessing the robustness of Multi-Armed Bandit algorithms against biased initialization

The robustness of Multi-Armed Bandit (MAB) algorithms forms a cornerstone of the efficacy of contemporary recommender systems. This study provides a comparative analysis of four widely-adopted MAB algorithmsEpsilon Greedy, Explore Then Commit (ETC), Upper Confidence Bound (UCB1), and Thompson Samplingunder the influence of biased initialization. Conducted in a simulated environment that mirrors practical recommender scenarios, the study examines the adaptive responses of these algorithms over time, quantifying their performance using cumulative regret as a primary metric. Our findings indicate varying degrees of resilience, with Epsilon Greedy exhibiting the slowest recovery from initial bias and Thompson Sampling demonstrating consistent adaptability. By exploring the implications of static biases to various multi-armed bandit algorithms, this research contributes foundational insights for advancing the development of robust and equitable recommender systems.

Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits

Adversarial attacks against stochastic multi-armed bandit (MAB) algorithms have been extensively studied in the literature. In this work, we focus on reward poisoning attacks and find most existing attacks can be easily detected by our proposed detection method based on the test of homogeneity, due to their aggressive nature in reward manipulations. This motivates us to study the notion of stealthy attack against stochastic MABs and investigate the resulting attackability. Our analysis shows that against two popularly employed MAB algorithms, UCB1 and $\epsilon$-greedy, the success of a stealthy attack depends on the environmental conditions and the realized reward of the arm pulled in the first round. We also analyze the situation for general MAB algorithms equipped with our attack detection method and find that it is possible to have a stealthy attack that almost always succeeds. This brings new insights into the security risks of MAB algorithms.

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health.

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.

Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search

Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i.e., the neighborhood of the solution, using randomized destroy heuristics and prioritized planning. Despite their recent success in various MAPF instances, current LNS-based approaches lack exploration and flexibility due to greedy optimization with a fixed neighborhood size which can lead to low-quality solutions in general. So far, these limitations have been addressed with extensive prior effort in tuning or offline machine learning beyond actual planning. In this paper, we focus on online learning in LNS and propose Bandit-based Adaptive LArge Neighborhood search Combined with Exploration (BALANCE). BALANCE uses a bi-level multi-armed bandit scheme to adapt the selection of destroy heuristics and neighborhood sizes on the fly during search. We evaluate BALANCE on multiple maps from the MAPF benchmark set and empirically demonstrate performance improvements of at least 50% compared to state-of-the-art anytime MAPF in large-scale scenarios. We find that Thompson Sampling performs particularly well compared to alternative multi-armed bandit algorithms.

Forced Exploration in Bandit Problems

The multi-armed bandit(MAB) is a classical sequential decision problem. Most work requires assumptions about the reward distribution (e.g., bounded), while practitioners may have difficulty obtaining information about these distributions to design models for their problems, especially in non-stationary MAB problems. This paper aims to design a multi-armed bandit algorithm that can be implemented without using information about the reward distribution while still achieving substantial regret upper bounds. To this end, we propose a novel algorithm alternating between greedy rule and forced exploration. Our method can be applied to Gaussian, Bernoulli and other subgaussian distributions, and its implementation does not require additional information. We employ a unified analysis method for different forced exploration strategies and provide problem-dependent regret upper bounds for stationary and piecewise-stationary settings. Furthermore, we compare our algorithm with popular bandit algorithms on different reward distributions.

Managerial decision-making: exploration strategies in dynamic environments

PurposeWe argue that a fundamental issue regarding how to search and how to switch between different cognitive modes lies in the decision rules that influence the dynamics of learning and exploration. We examine the search logics underlying these decision rules and propose conceptual prompts that can be applied mentally or computationally to aid managers’ decision-making.Design/methodology/approachBy applying Multi-Armed Bandit (MAB) modeling to simulate agents’ interaction with dynamic environments, we compared the patterns and performance of selected MAB algorithms under different configurations of environmental conditions.FindingsWe develop three conceptual prompts. First, the simple heuristic-based exploration strategy works well in conditions of low environmental variability and few alternatives. Second, an exploration strategy that combines simple and de-biasing heuristics is suitable for most dynamic and complex decision environments. Third, the uncertainty-based exploration strategy is more applicable in the condition of high environmental unpredictability as it can more effectively recognize deviated patterns.Research limitations/implicationsThis study contributes to emerging research on using algorithms to develop novel concepts and combining heuristics and algorithmic intelligence in strategic decision-making.Practical implicationsThis study offers insights that there are different possibilities for exploration strategies for managers to apply conceptually and that the adaptability of cognitive-distant search may be underestimated in turbulent environments.Originality/valueDrawing on insights from machine learning and cognitive psychology research, we demonstrate the fitness of different exploration strategies in different dynamic environmental configurations by comparing the different search logics that underlie the three MAB algorithms.

Intelligent Caching for Vehicular Dew Computing in Poor Network Connectivity Environments

In vehicular networks, some edge servers may not function properly due to the time-varying load condition and the uneven computing resource distribution, resulting in a low quality of caching services. To overcome this challenge, we develop a Vehicular dew computing (VDC) architecture for the first time by combining dew computing with vehicular networks, which can achieve wireless communication between vehicles in a resource-constrained environment. Consequently, it is crucial to develop an adaptive caching scheme that empowers vehicles to form efficient cooperation in VDC. In this paper, we propose an intelligent caching scheme based on VDC architecture, which includes two parts. First, to meet the dynamic nature of VDC, a spatiotemporal vehicle clustering algorithm is proposed to establish adaptive cooperation to assist content caching for vehicles. Second, the multi-armed bandit algorithm is employed to select suitable content for caching in vehicles based on real-time file popularity, and a model is established to dynamically update each vehicle’s request preferences. Extensive experiments are conducted to demonstrate that the proposed scheme has excellent performance in terms of cluster head stability and cache hit rate.

A Fairness-Enhanced Federated Learning Scheduling Mechanism for UAV-Assisted Emergency Communication.

As the frequency of natural disasters increases, the study of emergency communication becomes increasingly important. The use of federated learning (FL) in this scenario can facilitate communication collaboration between devices while protecting privacy, greatly improving system performance. Considering the complex geographic environment, the flexible mobility and large communication radius of unmanned aerial vehicles (UAVs) make them ideal auxiliary devices for wireless communication. Using the UAV as a mobile base station can better provide stable communication signals. However, the number of ground-based IoT terminals is large and closely distributed, so if all of them transmit data to the UAV, the UAV will not be able to take on all of the computation and communication tasks because of its limited energy. In addition, there is competition for spectrum resources among many terrestrial devices, and all devices transmitting data will bring about an extreme shortage of resources, which will lead to the degradation of model performance. This will bring indelible damage to the rescue of the disaster area and greatly threaten the life safety of the vulnerable and injured. Therefore, we use user scheduling to select some terrestrial devices to participate in the FL process. In order to avoid the resource waste generated by the terrestrial device resource prediction, we use the multi-armed bandit (MAB) algorithm for equipment evaluation. Considering the fairness issue of selection, we try to replace the single criterion with multiple criteria, using model freshness and energy consumption weighting as reward functions. The state of the art of our approach is demonstrated by simulations on the datasets.

Improvement of the recommendation system based on the multi-armed bandit algorithm

In order to effectively solve common problems of the recommendation system, such as the cold start problem and dynamic data modeling problem, the multi-armed bandit (MAB) algorithm, the collaborative filtering (CF) algorithm, and the user information feedback are applied by researchers to update the recommendation model online and in time. In other words, the cold start problem of the recommendation system is transformed into an issue of exploration and utilization. The MAB algorithm is used, user features are introduced as content, and the synergy between users is further considered. In this paper, the author studies the improvement of the recommendation system based on the multi-armed bandit algorithm. The Liner Upper Confidence Bound (LinUCB), Collaborative Filtering Bandits (COFIBA), and Context-Aware clustering of Bandits (CAB) algorithms are analyzed. It is found that the MAB algorithm can get a good maximum total revenue regardless of the content value after going through the cold start stage. In the case of a particularly large amount of content, the CAB algorithm achieves the greatest effect.

Investigation of progress and application related to Multi-Armed Bandit algorithms

This paper discusses four Multi-armed Bandit algorithms: Explore-then-Commit (ETC), Epsilon-Greedy, Upper Confidence Bound (UCB), and Thompson Sampling algorithm. ETC algorithm aims to spend the majority of rounds on the best arm, but it can lead to a suboptimal outcome if the environment changes rapidly. The Epsilon-Greedy algorithm is designed to explore and exploit simultaneously, while it often tries sub-optimal arm even after the algorithm finds the best arm. Thus, the Epsilon-Greedy algorithm performs well when the environment continuously changes. UCB algorithm is one of the most used Multi-armed Bandit algorithms because it can rapidly narrow the potential optimal decisions in a wide range of scenarios; however, the algorithm can be influenced by some specific pattern of reward distribution or noise presenting in the environment. Thompson Sampling algorithm is also one of the most common algorithms in the Multi-armed Bandit algorithm due to its simplicity, effectiveness, and adaptability to various reward distributions. The Thompson Sampling algorithm performs well in multiple scenarios because it explores and exploits simultaneously, but its variance is greater than the three algorithms mentioned above. Today, Multi-armed bandit algorithms are widely used in advertisement, health care, and website and app optimization. Finally, the Multi-armed Bandit algorithms are rapidly replacing the traditional algorithms; in the future, the advanced Multi-armed Bandit algorithm, contextual Multi-armed Bandit algorithm, will gradually replace the old one.

Survey of dynamic pricing based on Multi-Armed Bandit algorithms

Dynamic pricing seeks to determine the most optimal selling price for a product or service, taking into account factors like limited supply and uncertain demand. This study aims to provide a comprehensive exploration of dynamic pricing using the multi-armed bandit problem framework in various contexts. The investigation highlights the prevalence of Thompson sampling in dynamic pricing scenarios with a Bayesian backdrop, where the seller possesses prior knowledge of demand functions. On the other hand, in non-Bayesian situations, the Upper Confidence Bound (UCB) algorithm family gains traction due to their favorable regret bounds. As markets often exhibit temporal fluctuations, the domain of non-stationary multi-armed bandits within dynamic pricing emerges as crucial. Future research directions include enhancing traditional multi-armed bandit algorithms to suit online learning settings, especially those involving dynamic reward distributions. Additionally, merging prior insights into demand functions with contextual multi-armed bandit approaches holds promise for advancing dynamic pricing strategies. In conclusion, this study sheds light on dynamic pricing through the lens of multi-armed bandit problems, offering insights and pathways for further exploration.

Investigation of selection and application of Multi-Armed Bandit algorithms in recommendation system

The Multi-Armed Bandit (MAB) algorithm holds significant prominence as a recommendation system technique, effectively presenting user-centric content preferences based on the analysis of collected data. However, the application of the basic MAB algorithm in real-world recommendation systems is not without challenges, including issues related to data volume and data processing accuracy. Therefore, the optimization algorithm based on the MAB algorithm is more widely used in the recommendation system. This paper briefly introduces the multi-armed bandit algorithm, that is, the use of MAB in the recommendation system and the problems of the basic MAB algorithm. Aiming at the problems of the basic MAB algorithm, it introduces the MAB-based optimization algorithm used in the recommendation system. At the same time, this paper also analyzes and summarizes such algorithms. This paper introduces two different MAB-based optimization algorithms, namely The Details of Dynamic clustering based contextual combinatorial multi-armed bandit (DC3MAB) and Binary Upper Confidence Bound (BiUCB). In addition, this paper also introduces the application of algorithm in recommended system. Finally, this paper summarizes the introduced algorithms and proposes the future prospects for MAB optimization algorithms.

Exploring Multi-Armed Bandit algorithms: Performance analysis in dynamic environments

The Multi-armed Bandit algorithm, a proficient solver of the exploration-and-exploitation trade-off predicament, furnishes businesses with a robust tool for resource allocation that predominantly aligns with customer preferences. However, varying Multi-armed Bandit algorithm types exhibit dissimilar performance characteristics based on contextual variations. Hence, a series of experiments is imperative, involving alterations to input values across distinct algorithms. Within this study, three specific algorithms were applied, Explore-then-commit (ETC), Upper Confident Bound (UCB) and its asymptotically optimal variant, and Thompson Sampling (TS), to the extensively utilized MovieLens dataset. This application aimed to gauge their effectiveness comprehensively. The algorithms were translated into executable code, and their performance was visually depicted through multiple figures. Through cumulative regret tracking within defined conditions, algorithmic performance was scrutinized, laying the groundwork for subsequent parameter-based comparisons. A dedicated experimentation framework was devised to evaluate the robustness of each algorithm, involving deliberate parameter adjustments and tailored experiments to elucidate distinct performance nuances. The ensuing graphical depictions distinctly illustrated Thompson Sampling's persistent minimal regrets across most scenarios. UCB algorithms displayed steadfast stability. ETC manifested excellent performance with a low number of runs but escalate significantly along the number of runs growing. It also warranting constraints on exploratory phases to mitigate regrets. This investigation underscores the efficacy of Multi-armed Bandit algorithms while elucidating their nuanced behaviors within diverse contextual contingencies.

Investigation of frontier Multi-Armed Bandit algorithms and applications

Since the amount of content online is growing exponentially and people's time is limited, there is an urgent need for a high-performance algorithm that can make effective recommendations. This paper will introduce a recommendation system model, a sequential decision model, which is called Multi-Armed Bandit. The main idea of the Multi-Armed Bandit model is that at the beginning of the algorithm, all the recommended items are set to the same weight. In the subsequent recommendation process, the model explores the distribution of each item while changing the weight of each item according to the average revenue of each item, and selects more items with larger weight. This paper will introduce three cutting-edge Multi-Armed Bandit algorithms, their algorithmic ideas and their respective characteristics. The idea of Explore-Then-Commit (ETC) algorithm is to explore each item a certain number of times, and then select the best item for subsequent recommendation. The idea of the Upper Confidence Bound (UCB) algorithm is to represent the "exploration" and "exploitation" of each item by numerical values and add them to the UCB value, and select the item with the largest UCB value each time. The idea of TS is to first assume the distribution of each item, and then change the parameters of the distribution of each item based on the reward. At the end, this paper will introduce several scenarios where Multi-Armed Bandit algorithms can be used to give the reader an idea of how to use Multi-Armed Bandit algorithms.

An investigation of progress related to stochastic stationary bandit algorithms

The Multi-armed Bandit algorithm stands as a consequential tool for informed decision-making, distinct from reliance on intuitive selections, given its systematic proclivity to meticulously assess accessible alternatives with the intent of discerning the most auspicious outcome. Amid the repertoire of algorithmic variations, the Stochastic Stationary Bandit algorithm assumes a foundational and enduring role, finding versatile application across diverse domains, including but not limited to digital advertising, price optimization, and recommendation systems. With these considerations in view, the present study embarks upon a comprehensive scrutiny of this subject matter. This paper reviews on the Explore-Then-Commit algorithm, Upper Confidence Bound algorithm, and Thompson Sampling algorithm by explaining, comparing their formulation, features, and expected results. Explore-Then-Commit algorithm has distinct phase to explore all the choices uniformly. Upper Confidence Bound algorithm make decisions by calculate an upper confidence index which is an overestimate for each choice. Thompson Sampling algorithm depends on randomness to make choices. Explore-Then-Commit algorithm faces the problem of when to explore and when to stop. Upper Confidence Bound algorithm and Thompson Sampling algorithm solve this problem by avoid certain phases. Multi-armed Bandit algorithm could deal with the process of displaying items of potential interest to users in a recommendation system, the delivery of resources in resource allocation, or the way to maximize revenue in a business.

Optimization strategy for the pull arm function in the Multi-Armed Bandit algorithm

Within the domain of Multi-Armed Bandit (MAB) quandaries, the efficacy exhibited by the arm-pulling function stands as a determinant factor governing algorithmic performance. This study undertakes a systematic exploration of enhancing this function's optimization vis--vis the MovieLens dataset, with a specific focus on three prominent MAB algorithms: Upper Confidence Bound (UCB), Explore-Then-Commit (ETC), and Thompson Sampling (TS). The present exposition introduces an innovative methodology tailored to the Python 3.11 computational environment. Central to this approach is the pre-calculation of arm-specific reward frequencies, obviating the necessity for dataset look-up and thereby streamlining the reward selection process. Subsequently, a binomial distribution is fitted to these frequencies, ensuring rapid and accurate reward sampling. When benchmarked, the optimized version of the pull arm function showcased a remarkable reduction in execution time compared to their initial counterparts, all while maintaining algorithmic accuracy. The Thompson Sampling algorithm notably outperformed the others, exhibiting the lowest cumulative regret across diverse trial sizes. This research emphasizes the significance of refining core components in MAB algorithms and paves the way for practical enhancements in real-world applications.