#README.md#

# Q-learning
Using reinforcement learning to play snake and similar games.

An environment is created that simulates a game . This environment takes actions and returns the resulting screen of those actions, plus the resulting reward (-1 if the player dies, 0 if nothing happens and 1 if the player scores).

A player with a neural-network provides actions and learns from the environment responses using Q-learning per advantage learning. The network should learn what actions provide the best value. 

# Techniques used

* Advantage learning, particularized to Q-learning.
* Double Q-learning
* (Prioritized) memory replay
* Progressive discount rate growth
* Progressive exploration rate growth

# Results

For such a simple game the player should be able to learn to play for much longer, but it is clearly working:
[Neural network playing catch][catchgame]

# More info

[Demystifying Deep Reinforcement Learning](https://www.intelnervana.com/demystifying-deep-reinforcement-learning/)