Advances in Reinforcement Learning

Recent Advances in Reinforcement Learnin

📁 Recent Advances in Reinforcement Learning

✍ Leslie Pack Kaelbling 📂 Library 📅 1996 🌐 English

Reinforcement Learning: Optimal Feedback

📁 Reinforcement Learning: Optimal Feedback Control with Industrial Applications (Advances in Industrial Control)

✍ Jinna Li, Frank L. Lewis, Jialu Fan 📂 Library 📅 2023 🏛 Springer 🌐 English

This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-play

Reinforcement Learning: Optimal Feedback

📁 Reinforcement Learning: Optimal Feedback Control with Industrial Applications (Advances in Industrial Control)

✍ Jinna Li, Frank L. Lewis, Jialu Fan 📂 Library 📅 2023 🏛 Springer 🌐 English

This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-play

Transfer in Reinforcement Learning Domai

📁 Transfer in Reinforcement Learning Domains

✍ Matthew E. Taylor (auth.) 📂 Library 📅 2009 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often

Deep Reinforcement Learning in Action

📁 Deep Reinforcement Learning in Action

✍ Alexander Zai, Brandon Brown 📂 Library 📅 2020 🏛 Manning Publications 🌐 English

Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep

Deep Reinforcement Learning in Action

📁 Deep Reinforcement Learning in Action

✍ Alexander Zai, Brandon Brown 📂 Library 📅 2020 🏛 Manning Publications 🌐 English

Humans learn best from feedback-we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep