Multi-Agent Machine Learning: A Reinforcement Approach

Multi-Agent Machine Learning: A Reinforcement Approach

The e-book starts off with a bankruptcy on conventional tools of supervised studying, masking recursive least squares studying, suggest sq. errors tools, and stochastic approximation. bankruptcy 2 covers unmarried agent reinforcement studying. subject matters comprise studying price 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 options. quite a few algorithms and examples are awarded. bankruptcy four covers studying in multi-player video games, stochastic video games, and Markov video games, targeting 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 platforms, the evader pursuit video game, and the protecting a territory video games. bankruptcy 6 discusses new principles on studying inside of robot swarms and the leading edge proposal of the evolution of character traits.

• Framework for figuring out a number of tools and methods in multi-agent laptop learning.

• Discusses equipment of reinforcement studying reminiscent of a few types of multi-agent Q-learning

• Applicable to investigate professors and graduate scholars learning electric and laptop engineering, desktop technology, and mechanical and aerospace engineering

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