# Hyperparameters for Deep Q Learning


"""This file will be built with the intention of
having an individual file with just hyperparameters
so they can be modified easyli and without risk of 
messing up the mess source code """


# Descriptions of hyperparameters found on agent.py

#episodes - number of games the agent will play.

#gamma - decay or discount rate, to calculate the future discounted reward.

#epsilon - exploration rate, this is the rate in which an agent randomly decides its action rather than prediction.

# epsilon_decay - to decrease the number of explorations as it gets good at playing games.

# epsilon_min -we want the agent to explore at least this amount.

#learning_rate - For setting NN and avoid overfitting

#batch_size - Determines how much memory DQN will use to learn.

