Tootfinder

Deep Reinforcement Learning for Traveling Purchaser Problems
Haofeng Yuan, Rongping Zhu, Wanlu Yang, Shiji Song, Keyou You, Yuli Zhang
https://arxiv.org/abs/2404.02476

Deep Reinforcement Learning for Traveling Purchaser Problems
The traveling purchaser problem (TPP) is an important combinatorial optimization problem with broad applications. Due to the coupling between routing and purchasing, existing works on TPPs commonly address route construction and purchase planning simultaneously, which, however, leads to exact methods with high computational cost and heuristics with sophisticated design but limited performance. In sharp contrast, we propose a novel approach based on deep reinforcement learning (DRL), which addre…

A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
https://arxiv.org/abs/2404.03276

A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
The novel Internet of Things (IoT) paradigm is composed of a growing number of heterogeneous smart objects and services that are transforming architectures and applications, increasing systems' complexity, and the need for reliability and autonomy. In this context, both smart objects and services are often provided by third parties which do not give full transparency regarding the security and privacy of the features offered. Although machine-based Service Level Agreements (SLA) have been recen…

A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
Ahmed Abouelazm, Jonas Michel, J. Marius Zoellner
https://arxiv.org/abs/2405.01440

A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives and guide the agent toward the optimal policy. Since autonomous driving is a complex domain with partly conflicting objectives with varying degrees of priority, developing a suitable reward function represents a fundamental challenge. This paper aims to highlight the gap in such function design by assessing different pr…

MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search
Weizhe Chen, Sven Koenig, Bistra Dilkina
https://arxiv.org/abs/2404.03101

MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search
Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications. Because of the curse of dimensionality, the popular "centralized training decentralized execution" framework requires a long time in training, yet still cannot converge efficiently. In this paper, we propose a general training framework, MARL-LNS, to algorithmically address these issues by training on alternat…

Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking
Yinqiu Liu, Ruichen Zhang, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim
https://arxiv.org/abs/2404.01583

Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking
Performance optimization is a critical concern in networking, on which Deep Reinforcement Learning (DRL) has achieved great success. Nonetheless, DRL training relies on precisely defined reward functions, which formulate the optimization objective and indicate the positive/negative progress towards the optimal. With the ever-increasing environmental complexity and human participation in Next-Generation Networking (NGN), defining appropriate reward functions become challenging. In this article, …

A Gradually Reinforced Sample-Average-Approximation Differentiable Homotopy Method for a System of Stochastic Equations
Peixuan Li, Chuangyin Dang, Yang Zhan
https://arxiv.org/abs/2403.00294

A Gradually Reinforced Sample-Average-Approximation Differentiable Homotopy Method for a System of Stochastic Equations
This paper intends to apply the sample-average-approximation (SAA) scheme to solve a system of stochastic equations (SSE), which has many applications in a variety of fields. The SAA is an effective paradigm to address risks and uncertainty in stochastic models from the perspective of Monte Carlo principle. Nonetheless, a numerical conflict arises from the sample size of SAA when one has to make a tradeoff between the accuracy of solutions and the computational cost. To alleviate this issue, we…

This https://arxiv.org/abs/2310.14348 has been replaced.
link: https://scholar.google.com/scholar?q=a

DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraints
The domain of safe multi-agent reinforcement learning (MARL), despite its potential applications in areas ranging from drone delivery and vehicle automation to the development of zero-energy communities, remains relatively unexplored. The primary challenge involves training agents to learn optimal policies that maximize rewards while adhering to stringent safety constraints, all without the oversight of a central controller. These constraints are critical in a wide array of applications. Moreov…

Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behavior and Adversarial Style Sampling for Assistive Tasks
Tayuki Osa, Tatsuya Harada
https://arxiv.org/abs/2403.00344

Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behavior and Adversarial Style Sampling for Assistive Tasks
Autonomous assistance of people with motor impairments is one of the most promising applications of autonomous robotic systems. Recent studies have reported encouraging results using deep reinforcement learning (RL) in the healthcare domain. Previous studies showed that assistive tasks can be formulated as multi-agent RL, wherein there are two agents: a caregiver and a care-receiver. However, policies trained in multi-agent RL are often sensitive to the policies of other agents. In such a case,…

This https://arxiv.org/abs/2401.09286 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deployable Reinforcement Learning with Variable Control Rate
Deploying controllers trained with Reinforcement Learning (RL) on real robots can be challenging: RL relies on agents' policies being modeled as Markov Decision Processes (MDPs), which assume an inherently discrete passage of time. The use of MDPs results in that nearly all RL-based control systems employ a fixed-rate control strategy with a period (or time step) typically chosen based on the developer's experience or specific characteristics of the application environment. Unfortunately, the s…

This https://arxiv.org/abs/2401.09286 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deployable Reinforcement Learning with Variable Control Rate
Deploying controllers trained with Reinforcement Learning (RL) on real robots can be challenging: RL relies on agents' policies being modeled as Markov Decision Processes (MDPs), which assume an inherently discrete passage of time. The use of MDPs results in that nearly all RL-based control systems employ a fixed-rate control strategy with a period (or time step) typically chosen based on the developer's experience or specific characteristics of the application environment. Unfortunately, the s…