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.
- Advantage learning, particularized to Q-learning.
- Double Q-learning
- (Prioritized) memory replay
- Progressive discount rate growth
- Progressive exploration rate growth
For such a simple game the player should be able to learn to play for much longer, but it is clearly working:
