Can I read Transfer Learning for Multiagent Reinforcement Learning Systems (Synthesis Lectures on Artificial Intelligence and Machine Learning) on EtoBox?

Transfer Learning for Multiagent Reinforcement Learning Systems (Synthesis Lectures on Artificial Intelligence and Machine Learning) by Felipe Felipe Leno da Silva; Anna Helena Reali Anna Helena Reali Costa is a nonfiction available to read on EtoBox.

What is Transfer Learning for Multiagent Reinforcement Learning Systems (Synthesis Lectures on Artificial Intelligence and Machine Learning) about?

Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multi

Who reads Transfer Learning for Multiagent Reinforcement Learning Systems (Synthesis Lectures on Artificial Intelligence and Machine Learning)?

Author

Felipe Felipe Leno da Silva; Anna Helena Reali Anna Helena Reali Costa

Publisher

Springer International Publishing : Imprint: Springer

Published

2021

Language

EN

ISBN

9783031004636

Category

nonfiction

Subjects

Science, Computer Science, Mathematics