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Hands-On Reinforcement Learning for Games
book

Hands-On Reinforcement Learning for Games

by Micheal Lanham
January 2020
Intermediate to advanced
432 pages
10h 18m
English
Packt Publishing
Content preview from Hands-On Reinforcement Learning for Games

Curiosity learning

Up until now, we have only ever considered external rewards given to the agent from the environment. Yet, we and other animals receive a wide variety of external and internal rewards. Internal rewards are often characterized by emotion or feeling. An agent could have an internal reward that gives it +1 every time it looks to some face, perhaps denoting some internal love or infatuation reward. These types of rewards are called intrinsic rewards and they represent rewards that are internal or self-derived by the agent. This has some powerful capabilities for everything from creating interesting motivated agents to enhancing an agent's learning ability.

It is the second way in which Unity introduced curiosity learning or ...

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Publisher Resources

ISBN: 9781839214936