<filename>learning/parameters.py
# Define Hyper-parameters
# Neural network
batch_size = 256
learning_rate = 0.001
lr_decay_active = True
lr_decay = 10000
lr_reset_scale = 0.75 # number between 0 and 1

# Q-Learning
memory_size = 500000
gamma = 0.9
num_episodes = 100000

# target network update/copy weights average
target_update_average_active = True
target_update_average_tau = 0.9

# Epsilon-greedy
eps_start = 1.0
eps_end = 0.05
eps_decay = 5000 * 35 # It wil reach the end at around 4 times this value. Around 7 hands per game, around 5 steps per hand. Averages 35 steps per episode.

# Reward system
top_reward = 10

# Binary reward
binary_reward_active = True

# Elo system
K = 20
initial_elo = 1500

# Frequencies
# Policy network copied to target network
target_network_freq = 750
# Saving model
saving_model_freq = 250
# Neural network copy
copy_weights_freq = 500000

# CNN input is 4 concatenated states
concatenated = False

# Type of training
training_type = 3 # Phase 2: same AI in multiple agents

# Type of rl
rl = 'DDQN' # DQN or DDQN

<filename>learning/parameters.py
# Define Hyper-parameters
# Neural network
batch_size = 256
learning_rate = 0.001
lr_decay_active = True
lr_decay = 10000
lr_reset_scale = 0.75 # number between 0 and 1

# Q-Learning
memory_size = 500000
gamma = 0.9
num_episodes = 100000

# target network update/copy weights average
target_update_average_active = True
target_update_average_tau = 0.9

# Epsilon-greedy
eps_start = 1.0
eps_end = 0.05
eps_decay = 5000 * 35 # It wil reach the end at around 4 times this value. Around 7 hands per game, around 5 steps per hand. Averages 35 steps per episode.

# Reward system
top_reward = 10

# Binary reward
binary_reward_active = True

# Elo system
K = 20
initial_elo = 1500

# Frequencies
# Policy network copied to target network
target_network_freq = 750
# Saving model
saving_model_freq = 250
# Neural network copy
copy_weights_freq = 500000

# CNN input is 4 concatenated states
concatenated = False

# Type of training
training_type = 3 # Phase 2: same AI in multiple agents

# Type of rl
rl = 'DDQN' # DQN or DDQN

# Define Hyper-parameters # Neural network # number between 0 and 1 # Q-Learning # target network update/copy weights average # Epsilon-greedy # It wil reach the end at around 4 times this value. Around 7 hands per game, around 5 steps per hand. Averages 35 steps per episode. # Reward system # Binary reward # Elo system # Frequencies # Policy network copied to target network # Saving model # Neural network copy # CNN input is 4 concatenated states # Type of training # Phase 2: same AI in multiple agents # Type of rl # DQN or DDQN