Deep reinforcement learning compared with Q-table learning applied to backgammon
Reinforcement learning attempts to mimic how humans react to their surrounding environment by giving feedback to software agents based on the actions they take. To test the capabilities of these agents, researches have long regarded board games as a powerful tool. This thesis compares two approaches...
| Main Authors: | , |
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| Format: | Others |
| Language: | English |
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KTH, Skolan för datavetenskap och kommunikation (CSC)
2016
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| Subjects: | |
| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186545 |