Playtesting Match 3 Games with PPO
| dc.contributor.author | Malec, Stanislaw | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för matematiska vetenskaper | sv |
| dc.contributor.examiner | Andersson, Adam | |
| dc.contributor.supervisor | Haghir Chehreghani, Morteza | |
| dc.date.accessioned | 2023-10-24T07:55:47Z | |
| dc.date.available | 2023-10-24T07:55:47Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2023 | |
| dc.description.abstract | The training of proximal policy optimization agents with action masking on stochastic match-3 environments is explored in this thesis. A performant, feature-rich match-3 simulator is developed, and experiments demonstrate improved performance over a random policy on both seen and unseen levels. Furthermore, the best generalization performance is achieved when training is done by sampling levels from a subset of levels. | |
| dc.identifier.coursecode | MVEX03 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/307253 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | Reinforcement learning, match-3 | |
| dc.title | Playtesting Match 3 Games with PPO | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Data science and AI (MPDSC), MSc |