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- Дата: 20-06-2021, 19:17
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Название: Transfer Learning for Multiagent Reinforcement Learning Systems
Автор: Felipe Leno da Silva, Anna Helena Reali Costa
Издательство: Morgan and Claypool
Год: 2021
Формат: PDF
Страниц: 131
Размер: 10 Mb
Язык: English
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.
Автор: Felipe Leno da Silva, Anna Helena Reali Costa
Издательство: Morgan and Claypool
Год: 2021
Формат: PDF
Страниц: 131
Размер: 10 Mb
Язык: English
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.