Reinforcement learning (RL)-based web GUI testing techniques have attracted significant attention in both academia and industry due to their ability to facilitate automatic and intelligent exploration of websites under test. Yet, the existing approaches ...

Demand-Responsive Transport (DRT) is an innovative mode of public transportation that focuses on individual passenger needs by offering customized transportation solutions. Most prior researches rely on historical passenger flow to generate static ...

Urban traffic congestion is an increasingly pressing issue and advanced solutions like intelligent traffic control systems are becoming unavoidable. This paper explores the application of reinforcement learning to enhance traffic flow and reduce ...

In large-scale metropolis, it is critical to efficiently allocate various resources such as electricity, medical care, and transportation to meet the living demands of citizens, according to the spatio-temporal distributions of resources and demands. ...

Achieving optimal order dispatching has been a long-standing challenge for online ride-hailing platforms. Early methods would make shortsighted matchings as they only consider order prices alone as the edge weights in the driver-order bipartite graph, ...

Active Voltage Control (AVC) on the Power Distribution Networks (PDNs) aims to stabilize the voltage levels to ensure efficient and reliable operation of power systems. With the increasing integration of distributed energy resources, recent efforts have ...

Learning to communicate (L2C) involves learning how, when, and with whom to communicate to enhance cooperation among agents under limited bandwidth. However, introducing L2C impedes the original learning tasks, resulting in slower learning and poor ...

Automated analog circuit design migration significantly alleviates the burden on designers in circuit sizing under various operating conditions. Conventional methods model the migration problem as black-box optimization, requiring excessive iterations of ...

In e-scooter sharing systems, users randomly select and use e-scooters based on inaccurate battery information. This simple rental policy leads to low profitability on two fronts. First, inaccurate battery information causes unexpected device shutdowns, ...

Cooperative multiagent reinforcement learning approaches are increasingly being used to make decisions in contested and dynamic environments, which tend to be wildly different from the environments used to train them. As such, there is a need for a more ...

Communication is an effective mechanism for coordinating the behaviors of multiple agents, broadening their views of the environment, and to support their collaborations. In the field of multi-agent deep reinforcement learning (MADRL), agents can improve ...

The Naphtha Cracking Center (NCC) is central to petrochemical feedstock production through the intricate process. It consists of receipt stage for unloading naphtha, blending stage for mixing naphtha, and furnace stage for producing marketable products. ...

Multi-Agent Reinforcement Learning (MARL) holds promise for complex real-world applications but faces challenges in sample efficiency and policy explainability. My dissertation aims to address these critical barriers, advancing MARL towards more ...

There has been a substantial increase in interest in the field of Reinforcement Learning (RL), particularly that of using it to solve problems involving cooperation between many different agents, examples include self driving cars, robot assistants and ...

Cooperative multi-agent reinforcement learning methods aim to learn effective collaborative behaviours of multiple agents performing complex tasks. However, existing MARL methods are commonly proposed for fairly small-scale multi-agent benchmark problems,...

Two-sided matching is a classic and well-studied problem. As the participants are usually not aware of the accurate preferences towards the other side, the model of competing bandits characterizes the process of learning uncertainty through interactions ...

In order to better handle the issue of real-time multi-intersection traffic signal coordinated control, we expect that multi-agent decision-making can benefit from the advantages of large sequence models. In this paper, we propose a method for multi-...

In a multi-agent setting, altruistic cooperation is costly yet socially desirable. As such, agents adapting through independent reinforcement learning struggle to converge to efficient, cooperative policies. Indirect reciprocity (IR) constitutes a ...

We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing ...

Benchmarks play an important role in the development of machine learning algorithms, with reinforcement learning (RL) research having been heavily influenced by the available environments. However, RL environments are traditionally run on the CPU, ...