Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition

2012 ◽  
Author(s):  
Alexander Jungmann ◽  
Bernd Kleinjohann
Keyword(s):  
Reinforcement Learning ◽  
Service Composition ◽  
Service Selection ◽  
Learning Techniques

scholarly journals Optimal Policies for Quantum Markov Decision Processes

2021 ◽  
Author(s):  
Ming-Sheng Ying ◽  
Yuan Feng ◽  
Sheng-Gang Ying
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Quantum Systems ◽  
Sequential Decision Making ◽  
Mathematical Framework ◽  
Sequential Decision ◽  
Learning Techniques ◽  
Optimal Policies ◽  
Markov Decision ◽  
Programming Algorithms

AbstractMarkov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

scholarly journals On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic

2020 ◽  
Author(s):  
Bryan P Bednarski ◽  
Akash Deep Singh ◽  
William M Jones
Keyword(s):  
Public Health ◽  
Reinforcement Learning ◽  
Medical Equipment ◽  
Census Bureau ◽  
Learning Models ◽  
Public Health Emergencies ◽  
Medical Supplies ◽  
Learning Techniques ◽  
Disease Impact ◽  
Random States

Abstract objective This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic. materials and methods The system presented is simulated with disease impact statistics from the Institute of Health Metrics (IHME), Center for Disease Control, and Census Bureau[1, 2, 3]. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications. results The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93-95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74% (± 30.8) in simulations with 5 states to 93.50% (± 0.003) with 50 states. conclusion These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.

scholarly journals Dynamic Voltage and Frequency Scaling in NoCs with Supervised and Reinforcement Learning Techniques

2019 ◽  
Vol 68 (3) ◽  
pp. 375-389 ◽  
Author(s):  
Quintin Fettes ◽  
Mark Clark ◽  
Razvan Bunescu ◽  
Avinash Karanth ◽  
Ahmed Louri
Keyword(s):  
Reinforcement Learning ◽  
Frequency Scaling ◽  
Learning Techniques ◽  
Dynamic Voltage

scholarly journals Applying a Deep Q Network for OpenAIs Car Racing Game

2020 ◽  
Author(s):  
Ali Fakhry
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
State Of The Art ◽  
Learning Techniques ◽  
Car Racing ◽  
Custom Made ◽  
Learning Technique ◽  
Reward Threshold

The applications of Deep Q-Networks are seen throughout the field of reinforcement learning, a large subsect of machine learning. Using a classic environment from OpenAI, CarRacing-v0, a 2D car racing environment, alongside a custom based modification of the environment, a DQN, Deep Q-Network, was created to solve both the classic and custom environments. The environments are tested using custom made CNN architectures and applying transfer learning from Resnet18. While DQNs were state of the art years ago, using it for CarRacing-v0 appears somewhat unappealing and not as effective as other reinforcement learning techniques. Overall, while the model did train and the agent learned various parts of the environment, attempting to reach the reward threshold for the environment with this reinforcement learning technique seems problematic and difficult as other techniques would be more useful.

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