Curriculum learning for increasing the performance of a reinforcement learning agent in a static first-person shooter game
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradient methods, proximal policy optimization, in a first-person shooter game with a static player. We investigated how curriculum learning can be used to increase performance of a reinforcement learning a...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2018
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236462 |