batokio/rl_text-flappy-bird

Implementation of Q-Learning and Expected SARSA algorithms to solve the Text-Flappy-Bird game

RL_text-flappy-bird

The notebook presents the implementation of the Q-Learning and Expected SARSA algorithms to solve the Text-Flappy-Bird game

Text flappy bird game

The implementation of the environment can be found here: https://gitlab-research.centralesupelec.fr/stergios.christodoulidis/text-flappy-bird-gym

Final model

Please find below the final model used to measure the performance of the agents:

Hyperparameters	Q-Learning	Expected SARSA
Step-size	0.5	0.5
Step-size decay	1.0	0.99999
Epsilon	0.05	0.05
Epsilon decay	0.99999	0.99999
Discount	1.0	0.9

Performance

The sum of rewards achieved by both agents:

Q-Learning: 8,041,130
Expected SARSA : 36,660