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Multi-Agent Machine Learning: A Reinforcement Approach by Howard M. Schwartz

By Howard M. Schwartz

Filenote: PDF retail from EBL. Publisher/EBL seem to have created it through taking their great epub and switched over to PDF + pagination instead of the common attractive PDF imprint.
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The ebook starts off with a bankruptcy on conventional equipment of supervised studying, overlaying recursive least squares studying, suggest sq. mistakes tools, and stochastic approximation. bankruptcy 2 covers unmarried agent reinforcement studying.

Topics comprise studying worth services, Markov video games, and TD studying with eligibility strains. bankruptcy three discusses participant video games together with participant matrix video games with either natural and combined recommendations. a number of algorithms and examples are awarded. bankruptcy four covers studying in multi-player video games, stochastic video games, and Markov video games, concentrating on studying multi-player grid games—two participant grid video games, Q-learning, and Nash Q-learning. bankruptcy five discusses differential video games, together with multi participant differential video games, actor critique constitution, adaptive fuzzy regulate and fuzzy interference structures, the evader pursuit online game, and the protecting a territory video games. bankruptcy 6 discusses new rules on studying inside of robot swarms and the leading edge concept of the evolution of character traits.

• Framework for knowing various tools and techniques in multi-agent laptop learning.
• Discusses tools of reinforcement studying reminiscent of a few sorts of multi-agent Q-learning
• acceptable to investigate professors and graduate scholars learning electric and machine engineering, desktop technology, and mechanical and aerospace engineering

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Additional resources for Multi-Agent Machine Learning: A Reinforcement Approach

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The interesting aspect of reinforcement learning, as well as unsupervised learning methods, is the choice of rewards. In this chapter, we will discuss some of the fundamental ideas in reinforcement learning which we will refer to in the rest of the book. We will start with the simple -armed bandit problem and then present ideas on the meaning of the “value” function. 1 Introduction Reinforcement learning is learning to map situations to actions so as to maximize a numerical reward [1]. Without knowing which actions to take, the learner must discover which actions yield the most reward by trying them.

The goal is for the estimate to minimize the cost function . To find the “optimal” value of the parameter estimate , one takes the partial derivative of the cost function with respect to and sets this derivative to zero. 6 where the inverse, exists. If the inverse does not exist, then the system is not identifiable. For example, if in the straight line case one only had a single point, then the inverse would not span the two-dimensional space and it would not exist. One needs at least two independent points to draw a straight line.

Farhang-Boroujeny, Adaptive Filters: Theory and Applications. , 1998. [3] H. Robbins and S. Monro, “A stochastic approximation method,” Annals of Mathematical Statistics, vol. 22, no. 3, pp. 400–407, 1951. [4] J. Wolfowitz, “On the stochastic approximation method of robbins and monro,” Annals of Mathematical Statistics, vol. 23, no. 3, pp. 457–461, 1952. [5] H. J. Kushner and G. G. Yin, Recursive Approximation and Recursive Algorithms and Applications. , 2003. 49 Chapter 2 Single-Agent Reinforcement Learning The objective of this chapter is to introduce the reader to reinforcement learning.

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