<filename>base_config.py
class QL_Config(object):
    train_num_steps = 200       # number of steps for training
    train_start = 0             # how many steps of observation before beginning training
    print_freq = 50             # how often to print loss and other training statistics (training steps)

    test_freq = 100             # how often to evaluate model (training steps)
    test_num_episodes = 30      # how many episodes to test for
    num_test_to_print = 1       # number of test trajectories to print. set to 0 to never print

    gpu = -1                    # which GPU to use. set to -1 to use CPU only

    # TODO: make these unique per experiment
    log_dir = 'logs/'           # path to directory for TensorBoard summaries
    ckpt_dir = 'ckpts/'         # path to directory for checkpoints
    ckpt_freq = 10000           # how often (in train steps) to create a checkpoint of model weights, set to None to not save regular checkpoints

    gamma = 0.9                 # discount factor
    lr = 0.01                   # learning rate

    grad_clip = False           # whether or not to clip gradients
    clip_val = 5                # what value to clip the gradients to (if grad_clip == True)
    fc_reg = 1e-3               # L2-regularization for hidden layer weights (not biases), set to 0 for no regularization

    batch_size = 20             # batch size
    widths = [50, 50, 50]       # hidden layer sizes (set to [] for linear)

    q_values_metrics_size = 1000 # size of moving window for computing statistics of q_values
    target_update_freq = 1000
    replay_buffer_size = 100    # number of (s,a,r,s') tuples to keep in replay buffer
    use_double_q = True
    use_target_q_as_player2 = True

    cheating = False
    bomb_reward = -2.
    alive_reward = 0.1
    helper_reward_hl = 40000

    eps_begin  = 1              # starting value of epsilon for e-greedy
    eps_end    = 0.1            # ending value of epsilon decay
    eps_delay = 20000
    eps_nsteps = train_num_steps/2 # number of steps of epsilon decay
    test_epsilon = 0.0          # epsilon for test-time exploration (0 for always choosing the best action)

    num_players = 2             # number of players in game
    colors = ['red', 'white']   # suit colors
    cards_per_player = 3        # maximum number of cards in each player's hand
    max_number = 3              # number of the highest card
    number_counts = [3, 2, 2]   # number of cards with each number, starting with 1

<filename>base_config.py
class QL_Config(object):
    train_num_steps = 200       # number of steps for training
    train_start = 0             # how many steps of observation before beginning training
    print_freq = 50             # how often to print loss and other training statistics (training steps)

    test_freq = 100             # how often to evaluate model (training steps)
    test_num_episodes = 30      # how many episodes to test for
    num_test_to_print = 1       # number of test trajectories to print. set to 0 to never print

    gpu = -1                    # which GPU to use. set to -1 to use CPU only

    # TODO: make these unique per experiment
    log_dir = 'logs/'           # path to directory for TensorBoard summaries
    ckpt_dir = 'ckpts/'         # path to directory for checkpoints
    ckpt_freq = 10000           # how often (in train steps) to create a checkpoint of model weights, set to None to not save regular checkpoints

    gamma = 0.9                 # discount factor
    lr = 0.01                   # learning rate

    grad_clip = False           # whether or not to clip gradients
    clip_val = 5                # what value to clip the gradients to (if grad_clip == True)
    fc_reg = 1e-3               # L2-regularization for hidden layer weights (not biases), set to 0 for no regularization

    batch_size = 20             # batch size
    widths = [50, 50, 50]       # hidden layer sizes (set to [] for linear)

    q_values_metrics_size = 1000 # size of moving window for computing statistics of q_values
    target_update_freq = 1000
    replay_buffer_size = 100    # number of (s,a,r,s') tuples to keep in replay buffer
    use_double_q = True
    use_target_q_as_player2 = True

    cheating = False
    bomb_reward = -2.
    alive_reward = 0.1
    helper_reward_hl = 40000

    eps_begin  = 1              # starting value of epsilon for e-greedy
    eps_end    = 0.1            # ending value of epsilon decay
    eps_delay = 20000
    eps_nsteps = train_num_steps/2 # number of steps of epsilon decay
    test_epsilon = 0.0          # epsilon for test-time exploration (0 for always choosing the best action)

    num_players = 2             # number of players in game
    colors = ['red', 'white']   # suit colors
    cards_per_player = 3        # maximum number of cards in each player's hand
    max_number = 3              # number of the highest card
    number_counts = [3, 2, 2]   # number of cards with each number, starting with 1

# number of steps for training # how many steps of observation before beginning training # how often to print loss and other training statistics (training steps) # how often to evaluate model (training steps) # how many episodes to test for # number of test trajectories to print. set to 0 to never print # which GPU to use. set to -1 to use CPU only # TODO: make these unique per experiment # path to directory for TensorBoard summaries # path to directory for checkpoints # how often (in train steps) to create a checkpoint of model weights, set to None to not save regular checkpoints # discount factor # learning rate # whether or not to clip gradients # what value to clip the gradients to (if grad_clip == True) # L2-regularization for hidden layer weights (not biases), set to 0 for no regularization # batch size # hidden layer sizes (set to [] for linear) # size of moving window for computing statistics of q_values # number of (s,a,r,s') tuples to keep in replay buffer # starting value of epsilon for e-greedy # ending value of epsilon decay # number of steps of epsilon decay # epsilon for test-time exploration (0 for always choosing the best action) # number of players in game # suit colors # maximum number of cards in each player's hand # number of the highest card # number of cards with each number, starting with 1