Tootfinder

Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour
Emma M. A. Harrison
https://arxiv.org/abs/2507.13277

Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour
Robots are increasingly integrated across industries, particularly in healthcare. However, many valuable applications for quadrupedal robots remain overlooked. This research explores the effectiveness of three reinforcement learning algorithms in training a simulated quadruped robot for autonomous navigation and obstacle avoidance. The goal is to develop a robotic guide dog simulation capable of path following and obstacle avoidance, with long-term potential for real-world assistance to guide d…

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Libo Zhang, Yang Chen, Toru Takisaka, Kaiqi Zhao, Weidong Li, Jiamou Liu
https://arxiv.org/abs/2506.14125

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framework, SCRL, to address this problem. We formalize situational constraints as logic implications and develop a new algorithm that dynamically penalizes constraint violations. To handle situational constraints effectively, we propose a probabilistic selection mechanism to overcome li…

Online Training and Pruning of Deep Reinforcement Learning Networks
Valentin Frank Ingmar Guenter, Athanasios Sideris
https://arxiv.org/abs/2507.11975 http…

Online Training and Pruning of Deep Reinforcement Learning Networks
Scaling deep neural networks (NN) of reinforcement learning (RL) algorithms has been shown to enhance performance when feature extraction networks are used but the gained performance comes at the significant expense of increased computational and memory complexity. Neural network pruning methods have successfully addressed this challenge in supervised learning. However, their application to RL is underexplored. We propose an approach to integrate simultaneous training and pruning within advance…

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Hexian Ni, Tao Lu, Haoyuan Hu, Yinghao Cai, Shuo Wang
https://arxiv.org/abs/2506.14648

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Preference-based Reinforcement Learning (PbRL) methods provide a solution to avoid reward engineering by learning reward models based on human preferences. However, poor feedback- and sample- efficiency still remain the problems that hinder the application of PbRL. In this paper, we present a novel efficient query selection and preference-guided exploration method, called SENIOR, which could select the meaningful and easy-to-comparison behavior segment pairs to improve human feedback-efficiency…

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning
Fateme Salehi, Aamir Mahmood, Sarder Fakhrul Abedin, Kyi Thar, Mikael Gidlund
https://arxiv.org/abs/2507.12031

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning
6G networks are composed of subnetworks expected to meet ultra-reliable low-latency communication (URLLC) requirements for mission-critical applications such as industrial control and automation. An often-ignored aspect in URLLC is consecutive packet outages, which can destabilize control loops and compromise safety in in-factory environments. Hence, the current work proposes a link adaptation framework to support extreme reliability requirements using the soft actor-critic (SAC)-based deep rei…

LLM-based Dynamic Differential Testing for Database Connectors with Reinforcement Learning-Guided Prompt Selection
Ce Lyu, Minghao Zhao, Yanhao Wang, Liang Jie
https://arxiv.org/abs/2506.11870

LLM-based Dynamic Differential Testing for Database Connectors with Reinforcement Learning-Guided Prompt Selection
Database connectors are critical components enabling applications to interact with underlying database management systems (DBMS), yet their security vulnerabilities often remain overlooked. Unlike traditional software defects, connector vulnerabilities exhibit subtle behavioral patterns and are inherently challenging to detect. Besides, nonstandardized implementation of connectors leaves potential risks (a.k.a. unsafe implementations) but is more elusive. As a result, traditional fuzzing method…

Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
Mani Hamidi, Terrence W. Deacon
https://arxiv.org/abs/2507.11482 https://

Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
Three core tenets of reinforcement learning (RL)--concerning the definition of agency, the objective of learning, and the scope of the reward hypothesis--have been highlighted as key targets for conceptual revision, with major implications for theory and application. We propose a framework, inspired by open-ended evolutionary theory, to reconsider these three "dogmas." We revisit each assumption and address related concerns raised alongside them. To make our arguments relevant to RL as a model …

Generalised Rate Control Approach For Stream Processing Applications
Ziren Xiao
https://arxiv.org/abs/2506.11710 https://arxiv.org/pd…

Generalised Rate Control Approach For Stream Processing Applications
Distributed stream processing systems are widely deployed to process real-time data generated by various devices, such as sensors and software systems. A key challenge in the system is overloading, which leads to an unstable system status and consumes additional system resources. In this paper, we use a graph neural network-based deep reinforcement learning to collaboratively control the data emission rate at which the data is generated in the stream source to proactively avoid overloading scen…

Reinforcement Learning for Automated Cybersecurity Penetration Testing
Daniel L\'opez-Montero, Jos\'e L. \'Alvarez-Aldana, Alicia Morales-Mart\'inez, Marta Gil-L\'opez, Juan M. Au\~n\'on Garc\'ia
https://arxiv.org/abs/2507.02969

Reinforcement Learning for Automated Cybersecurity Penetration Testing
This paper aims to provide an innovative machine learning-based solution to automate security testing tasks for web applications, ensuring the correct functioning of all components while reducing project maintenance costs. Reinforcement Learning is proposed to select and prioritize tools and optimize the testing path. The presented approach utilizes a simulated webpage along with its network topology to train the agent. Additionally, the model leverages Geometric Deep Learning to create priors …

Application of LLMs to Multi-Robot Path Planning and Task Allocation
Ashish Kumar
https://arxiv.org/abs/2507.07302 https://arxiv.org/…

Application of LLMs to Multi-Robot Path Planning and Task Allocation
Efficient exploration is a well known problem in deep reinforcement learning and this problem is exacerbated in multi-agent reinforcement learning due the intrinsic complexities of such algorithms. There are several approaches to efficiently explore an environment to learn to solve tasks by multi-agent operating in that environment, of which, the idea of expert exploration is investigated in this work. More specifically, this work investigates the application of large-language models as expert …