scholarly journals Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach

2019 ◽  
Vol 8 (5) ◽  
pp. 1345-1348 ◽  
Author(s):  
Tran The Anh ◽  
Nguyen Cong Luong ◽  
Dusit Niyato ◽  
Dong In Kim ◽  
Li-Chun Wang
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Learning Approach ◽  
Mobile Crowd

scholarly journals Inter-Task Similarity for Lifelong Reinforcement Learning in Heterogeneous Tasks

2021 ◽  
Author(s):  
Sergio A. Serrano
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Lifelong Learning ◽  
Learning Approach ◽  
Trial And Error ◽  
Learning Paradigm ◽  
Service Robotics ◽  
State Action ◽  
Task Similarity ◽  
High Degree

Reinforcement learning (RL) is a learning paradigm in which an agent interacts with the environment it inhabits to learn in a trial-and-error way. By letting the agent acquire knowledge from its own experience, RL has been successfully applied to complex domains such as robotics. However, for non-trivial problems, training an RL agent can take very long periods of time. Lifelong machine learning (LML) is a learning setting in which the agent learns to solve tasks sequentially, by leveraging knowledge accumulated from previously solved tasks to learn better/faster in a new one. Most LML works heavily rely on the assumption that tasks are similar to each other. However, this may not be true for some domains with a high degree of task-diversity that could benefit from adopting a lifelong learning approach, e.g., service robotics. Therefore, in this research we will address the problem of learning to solve a sequence of RL heterogeneous tasks (i.e., tasks that differ in their state-action space).

scholarly journals Deep Reinforcement Learning of Transition States

2021 ◽  
Author(s):  
Jun Zhang ◽  
Yao-Kun Lei ◽  
Zhen Zhang ◽  
Xu Han ◽  
Maodong Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Reinforcement Learning ◽  
Transition State ◽  
Chemical Reaction ◽  
Reaction Mechanisms ◽  
Md Simulations ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Chemical Reaction Mechanisms

Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach, called RL‡, to automatically unravel chemical reaction mechanisms. In RL‡, locating the transition state of a...

scholarly journals Reinforcement Learning-Based Television White Space Database

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 0947
Author(s):  
Armie E. Pakzad ◽  
Raine Mattheus Manuel ◽  
Jerrick Spencer Uy ◽  
Xavier Francis Asuncion ◽  
Joshua Vincent Ligayo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
High Frequency ◽  
Learning Approach ◽  
Very High Frequency ◽  
Machine Learning Technique ◽  
Secondary Users ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
Very High

Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-based TVWSDB. Reinforcement learning (RL) is a machine learning technique that focuses on what has been done based on mapping situations to actions to obtain the highest reward. The learning process was conducted by trying out the actions to gain the reward instead of being told what to do. The actions may directly affect the rewards and future rewards. Based on the results, this algorithm effectively searched the most optimal channel for the SUs in query with the minimum search duration. This paper presents the advantage of using a machine learning approach in TVWSDB with an accurate and faster-searching capability for the available TVWS channels intended for SUs.

scholarly journals TREATING EPILEPSY VIA ADAPTIVE NEUROSTIMULATION: A REINFORCEMENT LEARNING APPROACH

2009 ◽  
Vol 19 (04) ◽  
pp. 227-240 ◽  
Author(s):  
JOELLE PINEAU ◽  
ARTHUR GUEZ ◽  
ROBERT VINCENT ◽  
GABRIELLA PANUCCIO ◽  
MASSIMO AVOLI
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Optimization Problem ◽  
Treatment Strategies ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Eeg Signal ◽  
Learning Techniques ◽  
Modern Machine

This paper presents a new methodology for automatically learning an optimal neurostimulation strategy for the treatment of epilepsy. The technical challenge is to automatically modulate neurostimulation parameters, as a function of the observed EEG signal, so as to minimize the frequency and duration of seizures. The methodology leverages recent techniques from the machine learning literature, in particular the reinforcement learning paradigm, to formalize this optimization problem. We present an algorithm which is able to automatically learn an adaptive neurostimulation strategy directly from labeled training data acquired from animal brain tissues. Our results suggest that this methodology can be used to automatically find a stimulation strategy which effectively reduces the incidence of seizures, while also minimizing the amount of stimulation applied. This work highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders such as epilepsy.

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