Tootfinder

Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
Ioannis Panitsas, Akrit Mudvari, Leandros Tassiulas
https://arxiv.org/abs/2403.08775 …

Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
In software-defined networking (SDN), the implementation of distributed SDN controllers, with each controller responsible for managing a specific sub-network or domain, plays a critical role in achieving a balance between centralized control, scalability, reliability, and network efficiency. These controllers must be synchronized to maintain a logically centralized view of the entire network. While there are various approaches for synchronizing distributed SDN controllers, most tend to prioriti…

Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control
Mike Timmerman, Aryan Patel, Tim Reinhart
https://arxiv.org/abs/2403.07216 htt…

Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control
The paper presents a technique using reinforcement learning (RL) to adapt the control gains of a quadcopter controller. Specifically, we employed Proximal Policy Optimization (PPO) to train a policy which adapts the gains of a cascaded feedback controller in-flight. The primary goal of this controller is to minimize tracking error while following a specified trajectory. The paper's key objective is to analyze the effectiveness of the adaptive gain policy and compare it to the performance of a s…

New Orleans signs: "Mama" edition, in honor of US Mother's Day.
1) Mama Rosa's, Rampart Street, French Quarter.
2) Nacho Mama's, Magazine Street, Uptown.
3) Mama Roux, Krewe du Vieux parade 2006 (first Carnival parade after the Hurricane Katrina Federal Flood).
4) I Bet Ya Mama Like It, food truck on Orleans Avenue, Mid-City.
Photographed by Infrogmation of New Orleans.
@…

The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments
Jingyu Xu, Weixiang Wan, Linying Pan, Wenjian Sun, Yuxiang Liu
https://arxiv.org/abs/2403.07923

The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments
In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocati…

Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand Manipulation
Lanpei Li, Enrico Donato, Vincenzo Lomonaco, Egidio Falotico
https://arxiv.org/abs/2404.04219

Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand Manipulation
Dexterous manipulation, often facilitated by multi-fingered robotic hands, holds solid impact for real-world applications. Soft robotic hands, due to their compliant nature, offer flexibility and adaptability during object grasping and manipulation. Yet, benefits come with challenges, particularly in the control development for finger coordination. Reinforcement Learning (RL) can be employed to train object-specific in-hand manipulation policies, but limiting adaptability and generalizability. …

Model-free $H_{\infty}$ control of It\^o stochastic system via off-policy reinforcement learning
Jing Guo Jing Guo, Xiushan Jiang, Weihai Zhang
https://arxiv.org/abs/2403.04412

Model-free $H_{\infty}$ control of Itô stochastic system via off-policy reinforcement learning
The stochastic $H_{\infty}$ control is studied for a linear stochastic Itô system with an unknown system model. The linear stochastic $H_{\infty}$ control issue is known to be transformable into the problem of solving a so-called generalized algebraic Riccati equation (GARE), which is a nonlinear equation that is typically difficult to solve analytically. Worse, model-based techniques cannot be utilized to approximately solve a GARE when an accurate system model is unavailable or prohibitively…

Structured Reinforcement Learning for Media Streaming at the Wireless Edge
Archana Bura, Sarat Chandra Bobbili, Shreyas Rameshkumar, Desik Rengarajan, Dileep Kalathil, Srinivas Shakkottai
https://arxiv.org/abs/2404.07315

Structured Reinforcement Learning for Media Streaming at the Wireless Edge
Media streaming is the dominant application over wireless edge (access) networks. The increasing softwarization of such networks has led to efforts at intelligent control, wherein application-specific actions may be dynamically taken to enhance the user experience. The goal of this work is to develop and demonstrate learning-based policies for optimal decision making to determine which clients to dynamically prioritize in a video streaming setting. We formulate the policy design question as a c…

Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
Sihan Zeng, Thinh T. Doan, Justin Romberg
https://arxiv.org/abs/2405.02456

Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
Multi-task reinforcement learning (RL) aims to find a single policy that effectively solves multiple tasks at the same time. This paper presents a constrained formulation for multi-task RL where the goal is to maximize the average performance of the policy across tasks subject to bounds on the performance in each task. We consider solving this problem both in the centralized setting, where information for all tasks is accessible to a single server, and in the decentralized setting, where a netw…

This https://arxiv.org/abs/2403.13245 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Federated reinforcement learning for robot motion planning with zero-shot generalization
This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i.e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments. We develop a federated reinforcement learning framework that enables collaborative learning of multiple learners and a central server, i.e., the Cloud, without sharing their raw data. In each iteration, each learner uploads its local control policy and the correspon…

Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty
Vincent W. Hill
https://arxiv.org/abs/2404.04699

Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty
This work describes a technique for active rejection of multiple independent and time-correlated stochastic disturbances for a nonlinear flexible inverted pendulum with cart system with uncertain model parameters. The control law is determined through deep reinforcement learning, specifically with a continuous actor-critic variant of deep Q-learning known as Deep Deterministic Policy Gradient, while the disturbance magnitudes evolve via independent stochastic processes. Simulation results are t…