Genetic Tuning for Smarter Deep Q-Learning

Risto Miikkulainen and Lex Fridman discussing the importance of integration of Evolutionary Computation in Deep Networks in certain tasks such as: assessing interpretability to very deep architectures (layer depth, topology, etc.) based on the hyperparameter tuning.

Name		Name	Last commit message	Last commit date
Latest commit History 127 Commits
.venv		.venv
__pycache__		__pycache__
model		model
.gitattributes		.gitattributes
ContrailOneRegular.ttf		ContrailOneRegular.ttf
LICENSE		LICENSE
README.md		README.md
agent.py		agent.py
agent_RL.py		agent_RL.py
game.py		game.py
game_user.py		game_user.py
genetic.py		genetic.py
model.py		model.py
old_begin_value_1min.gif		old_begin_value_1min.gif
old_beginning.gif		old_beginning.gif
old_good_value_5mins.gif		old_good_value_5mins.gif
old_old.gif		old_old.gif
plotme.py		plotme.py

roaked/snake-evolutionary-reinforcement-learning