Reinforcement Learning Algorithms in the Computer Game Flappy Bird

Author: Bc. Martin Glova
Supervisor: doc. RNDr. Gabriela Andrejková, CSc.
Co-supervisor: RNDr. Ľubomír Antoni, PhD.

?Objectives
?Bibliography
?Progress
?To Do List

Objectives

(1) To process already known results which use reinforcement learning algorithms for the computer game Flappy Bird.

(2) To investigate the already known reinforcement learning algorithms to apply them for the game Flappy Bird. Introduce new algorithms for the game focusing on the applicability of the neural networks.

(3) To implement the introduced algorithms and compare them with the known reinforcement learning algorithms for the game Flappy Bird focusing on the quality and the time complexity of the learning.

Bibliography

(1) N. Appiah and S. Vare. “Playing FlappyBird with Deep Reinforcement Learning”. In: Technical Report (2016).

(2) K. Chen. “Deep Reinforcement Learning for Flappy Bird”. In: Technical Report (2015).

(3) G. A. Rummery and M. Niranjan. “On-Line Q-Learning using connectionist systems”. In: CUED/F-INFENG/TR 166 (1994).

(4) Sarvagya Vaish. Flappy Bird RL. URL : http://sarvagyavaish.github.io/FlappyBirdRL/.

(5) Sourabh Verma. FlapPy Bird. URL : https://github.com/sourabhv/FlapPyBird.

(6) M. H. Hassoun: Fundamentals of artificial neural networks. MIT Press, Cambridge, 1995.

(7) S. Rogers, M. Girolami: A First Course in Machine Learning. Chapman & Hall/CRC, 2012.

Progress

Processing of the related works

Theoretical part

Own proposed solution

Implementation of the solution

Reinforcement Learning Algorithms in the Computer Game Flappy Bird

Objectives

Bibliography

Progress

To Do List