Playing a 3D Tunnel Game Using Reinforcement Learning
| Název práce v češtině: | Hraní 3D tunelové hry pomocí zpětnovazebního učení |
|---|---|
| Název v anglickém jazyce: | Playing a 3D Tunnel Game Using Reinforcement Learning |
| Klíčová slova: | tunnel game|reinforcement learning|artificial intelligence|algorithms |
| Klíčová slova anglicky: | tunnel game|reinforcement learning|artificial intelligence|algorithms |
| Akademický rok vypsání: | 2021/2022 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra softwaru a výuky informatiky (32-KSVI) |
| Vedoucí / školitel: | Adam Dingle, M.Sc. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 14.04.2022 |
| Datum zadání: | 15.04.2022 |
| Datum potvrzení stud. oddělením: | 12.05.2023 |
| Datum a čas obhajoby: | 29.06.2023 09:00 |
| Datum odevzdání elektronické podoby: | 12.05.2023 |
| Datum odevzdání tištěné podoby: | 15.05.2023 |
| Datum proběhlé obhajoby: | 29.06.2023 |
| Oponenti: | RNDr. Milan Straka, Ph.D. |
| Zásady pro vypracování |
| Tunnel games are a 3D video game genre in which the player advances through a tunnel and attempts to avoid obstacles by rotating around the tunnel. The student will write a tunnel game using the open-source Godot game engine. In this game, the player will avoid traps with a variety of shapes, will be able to shoot several types of creatures, and will need to collect enough energy to avoid running out of it. After that, the student will create agents that attempt to learn to play the game using reinforcement learning techniques. Some of these agents will use discrete tabular methods, and others will use linear or non-linear approximate methods, possibly using an artificial neural network. The input to the agents will be either the internal game state or, possibly, the 3D pixel data visible to the game's player. The student will compare and analyze the performance of these agents. |
| Seznam odborné literatury |
| Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
Mnih, V., Kavukcuoglu, K., et al (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Morales, M. (2020). Grokking Deep Reinforcement Learning. Manning Publications. |