Using Reinforcement Learning for Games with Nondeterministic State Transitions

Using Reinforcement Learning for Games with Nondeterministic State Transitions

Given the recent advances within a subfield of machine learning called reinforcement learning, several papers have shown that it is possible to create self-learning digital agents, agents that take actions and pursue strategies in complex environments without any prior knowledge. This thesis investi...

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Bibliographic Details
Main Author: Fischer, Max
Format: Others
Language:English
Published: Linköpings universitet, Statistik och maskininlärning 2019
Subjects:
reinforcement learning
proximal policy optimization
PPO
machine learning
artificial intelligence
deep learning
neural network
candy crush
mobile game
Computer Sciences
Datavetenskap (datalogi)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158523

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158523

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