Proceedings ArticleDOI
Markov game based control: Worst case design strategies for games against nature
Hitesh Shah,Madan Gopal +1 more
- Vol. 3, pp 339-343
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TLDR
The problem of decision making under uncertainty is formulated as a game in which the opponent is “disinterested” and plays at random, while the agent forces to pick a strategy that maximizes the probability of wining.Abstract:
For sequential design processes, the min-max strategy minimizes the worst-case performance cost. This is a game against nature, where the agent attempts to minimize a specified cost criterion, while nature attempts to maximize it. In this paper, we formulate the problem of decision making under uncertainty as a game in which the opponent (nature) is “disinterested” and plays at random, while the agent forces to pick a strategy that maximizes the probability of wining. The potency of proposed worst-case design strategy for games against nature has been established through simulation experiments on inverted-pendulum swing-up. Simulation results show the accelerated learning, and better relative stability of the system.read more
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Journal ArticleDOI
Distributed reinforcement learning algorithm of operator service slice competition prediction based on zero-sum markov game
TL;DR: A mass of numerical results prove that the min-max Q learning algorithm outperforms the repeated game, in which market state is invariable over time.
References
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
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Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Posted Content
Reinforcement Learning: A Survey
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Algorithms for Sequential Decision Making
TL;DR: This thesis shows how to answer the question ``What should I do now?
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