Pubben, Ilse (2020) QVA-learning for playing the game of Snake. Bachelor's Thesis, Artificial Intelligence.
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Abstract
In this thesis we will introduce a new reinforcement learning algorithm, QVA-learning, which is a combination of QV-learning and advantage updating. We will test this algorithm on the game of Snake and compare its performance with Q-learning and QV-learning. The state will be represented with vision grids of size 3×3, 5×5 and 7×7. We will also make use of an MLP as function approximator. We found that overall QVA-learning did not perform better than Q-learning or QV-learning. We also found that QVA-learning started learning earlier than the other algorithms when using a vision grid of size 7×7 (i.e., with more input nodes).
| Item Type: | Thesis (Bachelor's Thesis) |
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| Supervisor name: | Wiering, M.A. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 20 Jul 2020 10:08 |
| Last Modified: | 20 Jul 2020 10:08 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/22801 |
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