def test_pairs_with_dash(self):
assert Range("22-33").combos == DEUCE_COMBOS + THREE_COMBOS
def test_pairs_simple(self):
"""Test if pairs get all the combos."""
assert Range("22").combos == DEUCE_COMBOS
def test_pairs_multiple(self):
assert Range("22 33").combos == DEUCE_COMBOS + THREE_COMBOS
def test_empty_range(self):
assert Range().hands == tuple()
assert Range().combos == tuple()
assert Range("").hands == tuple()
assert Range("").combos == tuple()
def test_offsuit_and_suited_with_dash_reversed_is_the_same(self):
assert Range("J8-J4").hands == Range("J4-J8").hands
def test_suited_plus(self):
assert Range("KJs+").hands == (Hand("KJs"), Hand("KQs"))
def test_offsuit_plus(self):
assert Range("KJo+").hands == (Hand("KJo"), Hand("KQo"))
def __init__(self,
sess,
scope,
action_num=4,
state_shape=None,
hidden_layers_sizes=None,
reservoir_buffer_capacity=int(1e6),
anticipatory_param=0.1,
batch_size=256,
train_every=1,
rl_learning_rate=0.1,
sl_learning_rate=0.005,
min_buffer_size_to_learn=1000,
q_replay_memory_size=30000,
q_replay_memory_init_size=1000,
q_update_target_estimator_every=1000,
q_discount_factor=0.99,
q_epsilon_start=0.06,
q_epsilon_end=0,
q_epsilon_decay_steps=int(1e6),
q_batch_size=256,
q_train_every=1,
q_mlp_layers=None,
evaluate_with='average_policy'):
''' Initialize the NFSP agent.
Args:
sess (tf.Session): Tensorflow session object.
scope (string): The name scope of NFSPAgent.
action_num (int): The number of actions.
state_shape (list): The shape of the state space.
hidden_layers_sizes (list): The hidden layers sizes for the layers of
the average policy.
reservoir_buffer_capacity (int): The size of the buffer for average policy.
anticipatory_param (float): The hyper-parameter that balances rl/avarage policy.
batch_size (int): The batch_size for training average policy.
train_every (int): Train the SL policy every X steps.
rl_learning_rate (float): The learning rate of the RL agent.
sl_learning_rate (float): the learning rate of the average policy.
min_buffer_size_to_learn (int): The minimum buffer size to learn for average policy.
q_replay_memory_size (int): The memory size of inner DQN agent.
q_replay_memory_init_size (int): The initial memory size of inner DQN agent.
q_update_target_estimator_every (int): The frequency of updating target network for
inner DQN agent.
q_discount_factor (float): The discount factor of inner DQN agent.
q_epsilon_start (float): The starting epsilon of inner DQN agent.
q_epsilon_end (float): the end epsilon of inner DQN agent.
q_epsilon_decay_steps (int): The decay steps of inner DQN agent.
q_batch_size (int): The batch size of inner DQN agent.
q_train_step (int): Train the model every X steps.
q_mlp_layers (list): The layer sizes of inner DQN agent.
evaluate_with (string): The value can be 'best_response' or 'average_policy'
'''
self.use_raw = False
self._sess = sess
self._scope = scope
self._action_num = action_num
self._state_shape = state_shape
self._layer_sizes = hidden_layers_sizes
self._batch_size = batch_size
self._train_every = train_every
self._sl_learning_rate = sl_learning_rate
self._anticipatory_param = anticipatory_param
self._min_buffer_size_to_learn = min_buffer_size_to_learn
self._reservoir_buffer = ReservoirBuffer(reservoir_buffer_capacity)
self._prev_timestep = None
self._prev_action = None
self.evaluate_with = evaluate_with
self.d = {
0: 'A',
1: '2',
2: '3',
3: '4',
4: '5',
5: '6',
6: '7',
7: '8',
8: '9',
9: 'T',
10: 'J',
11: 'Q',
12: 'K'
}
self.s = {0: 's', 1: 'h', 2: 'd', 3: 'c'}
self.c2n = {
'2': 2,
'3': 3,
'4': 4,
'5': 5,
'6': 6,
'7': 7,
'8': 8,
'9': 9,
'T': 10,
'J': 11,
'Q': 12,
'K': 13,
'A': 14
}
self.late_range = Range(
'22+, A2s+, K2s+, Q2s+, J2s+, J8, T9, 98, 87, 76s, 65s, 54s, 98s+, K9+, Q8+, J7+, T6s+, A9+'
)
# Total timesteps
self.total_t = 0
# Step counter to keep track of learning.
self._step_counter = 0
with tf.variable_scope(scope):
# Inner RL agent
self._rl_agent = DQNAgent(
sess, scope + '_dqn', q_replay_memory_size,
q_replay_memory_init_size, q_update_target_estimator_every,
q_discount_factor, q_epsilon_start, q_epsilon_end,
q_epsilon_decay_steps, q_batch_size, action_num, state_shape,
q_train_every, q_mlp_layers, rl_learning_rate)
with tf.variable_scope('sl'):
# Build supervised model
self._build_model()
self.sample_episode_policy()