Below are some of the hyperparameters defined that we will be using throughout the code and are totally configurable. 

# Discount in Bellman Equation
gamma = 0.95 

# Epsilon
epsilon = 1.0

# Minimum Epsilon
epsilon_min = 0.01

# Decay multiplier for epsilon
epsilon_decay = 0.99

# Size of deque container
deque_len = 20000

# Average score needed over 100 epochs
target_score = 200

# Number of games
episodes = 2000

# Data points per episode used to train the agent
batch_size = 64

# Optimizer for training the agent
optimizer = 'adam'

# Loss for training the agent
loss = 'mse'

• gamma - Discount parameter in the bellman equation
• epsilon_decay - Multiplier by which you want to discount the value of 'epsilon' after each episode/game
• epsilon_min - Minimum value of 'epsilon' beyond which you do not want to decay it
• deque_len - Size of the deque container used to store the training examples (state, reward, done, and action)
• target_score - Average score over 100 epochs you want the agent to score after which you stop the learning process
• episodes - Maximum number of games you want the agent to play
• batch_size - Size of the batch of training data (stored in the deque container) used to train the agent after each episode
• optimizer - Optimizer of choice for training the agent
• loss - Loss of choice for training the agent