Service caching with multi-agent reinforcement learning in cloud-edge collaboration computing

Recently, the researches on multi-agent reinforcement learning (MARL) have attracted tremendous interest in many applications, especially for autonomous driving. The main problem of MARL is how to deal with the uncertainty in the environment and the interaction between the connected agents. To solve the problem, a distributed robust temporal differential deep Q-network algorithm (MARTD-DQN) was developed in this paper. MARTD-DQN consists of two parts, the decentralized MARL algorithm (DMARL) and the robust TD deep Q-network algorithm (RTD-DQN). DMARL improves the robustness of the policy estimation by fusing the states from the neighbors over communicated networks. RTD- DQN improves the robustness to outliers through on-line estimation of the uncertainty. By combining the two algorithms, the proposed algorithm can be robust not only to node failures but also to the outliers. Then the proposed algorithm is applied to ACC simulations of autonomous cars. The simulation results are given to show the efficiency of the proposed algorithm.

Aim and ApproachLarge cooling water system has great potential of energy saving due to improper operation control. The coupled hydraulic and thermodynamic characteristics among chillers, pumps, and cooling towers lead to the difficulty of cooling water system operation. This study investigates an advanced control approach of multi-agent reinforcement learning (RL) for a large cooling water system. The multiple RL agents interact with each other to deal with the complex coupled characteristics in the cooling water system. The proposed approach is implemented in a real-world cooling water system and validated with experiments.Scientific Innovation and RelevanceIn this study, a multi-agent RL approach is proposed to separately control the chillers, pumps, and cooling towers in the cooling water system. The soft actor critic algorithm is used in the RL agent, as it is an efficient and advanced RL algorithm for continuous and discrete control problems. The agents are designed to have private states, private actions, and shared rewards. The reward functions are carefully designed to balance the agents for global optima.To provide a realistic environment for RL training, a detailed physical modeling platform of a cooling water system is established and validated. This platform is capable of hydraulic and thermodynamic calculation of cooling water systems with customized numbers and characteristics of chillers, pumps, and cooling towers, which is scalable for different cooling water systems.The proposed multi-agent RL controller is implemented in the building automation system of a real cooling water system. A one-month experimental study of the multi-agent RL performance is conducted. The proposed multi-agent RL controller is compared with a single-agent RL controller and a rule-based controller through real-world experiments to demonstrate the energy performance. This is a pilot study of multi-agent RL approach to real-world cooling water system control.Preliminary Results and ConclusionsThe preliminary simulation study is conducted in the established cooling water system model. The electricity consumption simulation error is 4.0% in coefficient of variation of mean absolute error (CVMAE) compared with the measured 15-minute electricity consumption. The 6-month simulation shows that the single-agent RL can save 6.2% energy from the rule-based control, while the multi-agent RL can save 7.2% electricity consumption of the cooling water system. In the following study, the multi-agent RL implementation will be practiced with more experiments and applications for cooling water system control.

A survey on multi-agent reinforcement learning and its application

As cyber threats become increasingly automated and sophisticated, novel solutions must be introduced to improve defense of enterprise networks. Deep reinforcement learning (DRL) has demonstrated potential in mitigating these advanced threats. Single DRL agents have proven utility toward execution of autonomous cyber defense. Despite the success of employing single DRL agents, this approach presents significant limitations, especially regarding scalability within large enterprise networks. An attractive alternative to the single‐agent approach is the use of multi‐agent reinforcement learning (MARL). However, developing MARL agents is costly with few options for examining MARL cyber defense techniques against adversarial agents. This paper presents a MARL network security environment, the fourth iteration of the cyber autonomy gym for experimentation (CAGE) challenges. This challenge was specifically designed to test the efficacy of MARL algorithms in an enterprise network. Our work aims to evaluate the potential of MARL as a robust and scalable solution for autonomous network defense.

Reinforcement learning, a machine learning technique, has already shown advancement in solving complex sequential decision-making problems. Many tasks involve multiple agents and require sequential decision-making policies to achieve common goals, such as warehouse automation, autonomous driving, and game-playing. To derive the policies for all agents, these problems can be modelled as multi-agent systems and addressed by multi-agent reinforcement learning (MARL). However, optimizing policies in multi-agent scenarios presents significant challenges due to the intricate behaviours of multiple agents and the non-stationary nature of the environments’ complex dynamics. Firstly, optimizing policies in multi-agent scenarios presents significant challenges due to the complexity of multi-agent behaviours, especially in partially observable environments. Also, the dynamic nature of agents’ behaviours and their interactions with other agents lead to changes in the environment’s states and agents’ observations over time, which is more complicated in open environments. Furthermore, the need to balance individual and collective objectives in certain real-world multi-agent environments also complicates the decision-making process. This doctoral thesis aims to tackle the following three fundamental multi-agent research problems and propose a solution for each. Our research covers from theoretical analysis to practical application. We start by studying the problem of learning an efficient policy under partially observable environments. We notice that a group of agents cooperates to com- pete against another group of agents (named opponents), with the limitation that information about the opponents is inaccessible due to partial observability. To address this issue, we propose a novel multi-agent distributional actor-critic algorithm to achieve speculative opponent modeling with purely local information (i.e., the controlled agent’s observations, actions, and rewards). The actor maintains a speculated belief of the opponents, which we call the speculative opponent models, to predict opponents’ actions using local observations and make decisions accordingly. The distributional critic models the return distribution of the policy. It reflects the quality of the actor and thus can guide the training of the imaginary opponent model that the actor relies on. Extensive experiments confirm that our method successfully models opponents’ behaviours without their data and delivers superior performance against baseline methods with a faster convergence speed. Furthermore, in some environments, teammates’ numbers and policies change with the market, requiring workers to adapt to perform different sets of tasks across time. To solve this issue, we propose an RL-based method that allows the controlled agent to collaborate with dynamic teammates in open environments. The controlled agent maintains a dual teamwork situation inference model to capture the current teamwork state and facilitate reasonable decision-making under partial observability. Considering the dynamic types of teammates, we first leverage the Chinese Restaurant Process-based model to categorize versatile teammate policies into distinct clusters, which improves the efficiency of identifying current teamwork situations. Next, to model the heterogeneous relationships among agents and accommodate the varying number of teammates, we apply heterogeneous graph attention neural networks to learn the representation of the teamwork situation. Extensive experiments confirm that our method outperforms state-of-the-art baselines with faster convergence in various ad hoc teamwork tasks. Lastly, in certain real-world applications, such as routing problems and warehouse management, decision-makers must balance overall benefits with individual fairness among agents. Achieving both learning efficiency and fairness concurrently presents a complex, multi-objective, joint-policy optimization challenge. Moreover, existing approaches are predominantly confined to simulation environments. To address the above issues, we present a pioneering MARL approach to balance individual and collective objectives for agents collaboration. Extensive experiments on synthetic and real-world datasets demonstrate that our method not only surpasses the state-of-the-art DRL methods, but also exhibits advantages over established heuristics, notably in achieving significantly faster optimization speeds. This method underlines the essential integration of fairness into real-world applications, representing a major advancement in creating equitable and efficient logistics solutions. To conclude, this doctoral thesis investigates three fundamental multi-agent decision-making research problems that are ubiquitous and unsolved. The proposed three MARL methodology solutions achieve efficient policy training and performance for agents in multi-agent environments with uncertainties raised by the partially observable, the open environment, and the individual-collective objectives of MARL. This thesis delves into novel designs of various critical components of MARL, including MDP formulations, policy networks, training algorithms, and inference methods. These contributions significantly elevate the effectiveness and efficiency of cooperative MARL, establishing new performance benchmarks.

As cyber threats become increasingly automated and sophisticated, novel solutions must be introduced to improve defence of enterprise networks. Deep Reinforcement Learning (DRL) has demonstrated potential in mitigating these advanced threats. Single DRL Agents have proven utility toward execution of autonomous cyber defence. Despite the success of employing single DRL Agents, this approach presents significant limitations, especially regarding scalability within large enterprise networks. An attractive alternative to the single agent approach is the use of Multi-Agent Reinforcement Learning (MARL). However, developing MARL agents is costly with few options for examining MARL cyber defence techniques against adversarial agents. This paper presents a MARL network security environment, the fourth iteration of the Cyber Autonomy Gym for Experimentation (CAGE) challenges. This challenge was specifically designed to test the efficacy of MARL algorithms in an enterprise network. Our work aims to evaluate the potential of MARL as a robust and scalable solution for autonomous network defence.

DeepMAG: Deep reinforcement learning with multi-agent graphs for flexible job shop scheduling

Recent advancements in multi-agent reinforcement learning (MARL) have demonstrated success on various cooperative multi-agent tasks. However, current benchmarks often fall short of representing realistic scenarios that demand agents to execute sequential tasks over long temporal horizons while balancing multiple objectives. To address this limitation, we introduce multi-objective SMAC (MOSMAC), a comprehensive MARL benchmark designed to evaluate MARL methods on tasks involving multiple objectives, sequential subtask assignments, and varying temporal horizons. MOSMAC requires agents to tackle a series of interconnected subtasks in StarCraft II while simultaneously optimizing for multiple objectives, including combat, safety, and navigation. Through rigorous evaluation of nine state-of-the-art MARL algorithms, we demonstrate that MOSMAC presents substantial challenges to existing methods, particularly in long-horizon scenarios. Our analysis establishes MOSMAC as an essential benchmark for bridging the gap between single-objective MARL and multi-objective MARL (MOMARL). The codes for MOSMAC are available at:https://github.com/smu-ncc/mosmac.

Automated clash resolution for reinforcement steel design in concrete frames via Q-learning and Building Information Modeling

This paper investigates the error accumulation problem in centralized training and decentralized execution (CTDE) policy‐based multi‐agent reinforcement learning (MARL) algorithms, which arises from local observation inaccuracies. To address this issue, we propose a novel MARL algorithm that incorporates imitation learning using local observations. Firstly, by analysing the multi‐agent proximal policy optimization algorithm and examining the problems arising when global states are replaced with local observations, it is proved that insufficient observations can lead to information loss, thereby introducing errors of advantage function, and it is demonstrated that the generalized advantage estimation method accumulates errors during the training process. Then, imitation learning is introduced and a novel training framework that combines reinforcement learning and imitation learning is proposed. During the reinforcement learning phase, an MARL agent trained with global observations acts as an expert. Subsequently, imitation learning is applied to train another agent that mimics the expert's decisions using only local observations. Finally, the effectiveness of this algorithm is verified in some commonly used multi‐agent environments, which demonstrates its superior performance compared to traditional multi‐agent reinforcement learning algorithms.

Reinforcement learning (RL) and multi-agent reinforcement learning (MARL) are disciplines concerned with defining automatically the behaviour of an agent, or a set of interacting agents, by means of reward mechanisms coming from the environment. An important research issue in the context of RL and MARL is the definition of approaches to combine the knowledge of multiple learning agents to improve the overall performance of the multi-agent system (MAS). This paper illustrates how to improve RL and MARL algorithms by utilizing results from multi-linear algebra such as tensors and tensor factorizations. In particular, the focus is on showing how to modify existing algorithms from literature to include a tensor factorization step applied to the Q-Tables learned by the individual agents to generalize the knowledge about the actions performed in the environment. The modified algorithms are then evaluated in three RL and MARL scenarios against their unmodified version to show the benefits of the tensor factorization step.

Multi-agent reinforcement learning (MARL) enables us to create adaptive agents in challenging environments, even when the agents have limited observation. The cooperative multi-agent setting introduces numerous challenges compared to the single-agent setting, such as the moving target problem and the curse of dimensionality with respect to the action space. It also aggravates the credit assignment problem, as the credit is not only spread across a sequence of actions, but also multiple agents. This setting also introduces new possibilities, such as task parallelization, specialization, and communication. The Centralized Training with Decentralized Execution paradigm has emerged as a popular strategy to mitigate some of the difficulties in MARL, while still ensuring that the policy of the agents is only conditioned on their local history. However, how to fully leverage this paradigm is still an open question. During the first year of my Ph.D., I developed a novel algorithm, Local Advantage Networks (LAN), that proposes an alternative direction to value factorization, that is more scalable, not limited in its representation and state-of-the-art. The next parts of my research will focus on multi-agent exploration and learning to communicate.

Multi-agent reinforcement learning has gained great success in many decision-making tasks. However, there are still some challenges such as low efficiency of exploration, significant time consumption, which bring great obstacles for it to be applied in the real world. Incorporating human knowledge into the learning process has been regarded as a promising way to ameliorate these problems. This paper proposes a novel approach to utilize imperfect human knowledge to improve the performance of multi-agent reinforcement learning. We leverage logic rules, which can be seen as a popular form of human knowledge, as part of the action space in reinforcement learning. During the trial-and-error, the value of rules and the original action will be estimated. Logic rules, therefore, can be selected flexibly and efficiently to assist the learning. Moreover, we design a new exploration way, in which rules are preferred to be explored at the early training stage. Finally, we make experimental evaluations and analyses of our approach in challenging StarCraftII micromanagement scenarios. The empirical results show that our approach outperforms the state-of-the-art multi-agent reinforcement learning method, not only in the performance but also in the learning speed.

As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments. This work presents an approach for generating policy-level contrastive explanations for MARL to answer a temporal user query, which specifies a sequence of tasks completed by agents with possible cooperation. The proposed approach encodes the temporal query as a PCTL* logic formula and checks if the query is feasible under a given MARL policy via probabilistic model checking. Such explanations can help reconcile discrepancies between the actual and anticipated multi-agent behaviors. The proposed approach also generates correct and complete explanations to pinpoint reasons that make a user query infeasible. We have successfully applied the proposed approach to four benchmark MARL domains (up to 9 agents in one domain). Moreover, the results of a user study show that the generated explanations significantly improve user performance and satisfaction.

As one technique for autonomous driving, vehicular networks can achieve high efficiency with vehicle-and-infrastructure cooperation, bringing high safety and many value-added services. To achieve higher communication efficiency, much effort has been done to cope with the resource allocation issues for vehicular networks. Nevertheless, due to the strong nonconvexity and nonlinearity, the classical joint resource allocation problem in vehicular networks is typically NP-hard. The multiagent reinforcement learning (MARL) has emerged as a promising solution to tackle this challenge but its stability and scalability are not satisfactory when the amount of vehicles gets increased. In this article, we mainly investigate the issue of joint spectrum and power allocation in vehicular communication networks, and carefully consider the interactions between the vehicles and environment by incorporating the cooperative stochastic game theory with MARL, named complete-game MARL (CG-MARL), to achieve a better convergence and stability with the theoretical computational complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{N})$ </tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> denoting the dimension of action space and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> denoting the number of V2X Vehicular. Furthermore, the mean-field game (MFG) theory is employed to further enhance the MARL for decreasing the horrible computing resource consumption caused by the CG-MARL to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{2})$ </tex-math></inline-formula> while maintaining an approximate performance. The simulation results demonstrate that the proposed mean-field-aided MARL (MF-MARL) for vehicular network resource allocation can achieve 95% near-optimal performance with much lower complexity, which indicates its significant potentials in the scenarios with massive and dense vehicles.