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README.md

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-# Flappy Bird Genetic Algorithm
+# Flappy Bird Genetic Algorithm
+Train a neural network using genetic evolution to play flappy bird! 
+
+## Final Results
+![Trained Model](Screenshots/trained_final.gif?raw=true "Trained Flappy Bird")
+
+## Algorithm Details
+* Starts out with a pool of 50 models
+* Each iteration based on fitness scores of the models, perform crossover
+* Crossover would swap the first layers (input -> hidden) for both the selected parents
+* Random mutation ensures that the models are changed at every iteration
+
+## Progress Screenshots
+#### Stage 1
+Initially all the models would do the "same" wrong thing. So they would all die out quickly.
+![Untrained No-Spread](Screenshots/untrained_initial_states_nospread.gif?raw=true "Untrained No-Spread")
+
+#### Stage 2
+After some time however, they would start to show more variation, but still perform the wrong moves. This gives us a spread of flappy birds throughout the screen (lengthwise).
+![Untrained Spread](Screenshots/untrained_initial_states_spread.gif?raw=true "Untrained Spread")
+
+#### Stage 3
+After a bit of training (~1hr) the spread decreases and is more concentrated at the height where there is a hole in the pipes. They start performing a lot better since they now understand when to flap and when not to.
+![Trained](Screenshots/trained_set_initial.gif?raw=true "Trained")
+
+#### Stage 4
+Considering the model with the maximum fitness, we can consider it as a trained model and it would perform much better than the average human.
+![Final Trained Model](Screenshots/trained_final.gif?raw=true "Final Trained Model")
+
+### Disclaimer
+* Based on Flappy Bird clone in pygame, https://github.com/sourabhv/FlapPyBird