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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| Format: | Others |
| Language: | English |
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Linköpings universitet, Statistik och maskininlärning
2019
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158523 |