def test_action_indexing(self):
action_indexer = neural_game.ActionIndexer(['a', 'b', 'c', 'd'])
self.assertEqual(action_indexer.get_index('a'), 0)
self.assertEqual(action_indexer.get_index('d'), 3)
self.assertEqual(action_indexer.get_action(2), 'c')
def create_neural_leduc(cards, max_raises=4, raise_amount=2):
game = Leduc(cards, max_raises=max_raises, raise_amount=raise_amount)
action_indexer = neural_game.ActionIndexer([0, 1, 2])
# Now compute the state vectors. We first define a one-hot-encoding based on the cards.
card_indices = dict(enumerate(cards))
card_indices = {v: k for k, v in card_indices.items()}
state_vectors = compute_state_vectors(game.info_set_ids.values(), card_indices, max_raises)
info_set_vectoriser = neural_game.InfoSetVectoriser(state_vectors)
return neural_game.NeuralGame(game, action_indexer, info_set_vectoriser)
def test_initialise(self):
action_indexer = neural_game.ActionIndexer(['a', 'b'])
network = deep_cfr.DeepRegretNetwork(state_shape=(5, ),
action_indexer=action_indexer,
player=1)
with tf.Session() as sess:
network.set_sess(sess)
network.initialise()
# Check we can predict
network.predict_advantages(np.array([1.0, 2.0, 3.0, 4.0, 5.0]),
action_indexer)
def test_predict_advantages(self):
info_set_vector = np.array([1, 2, 3, 4, 5]).astype(np.float32)
action_indexer = neural_game.ActionIndexer(['a', 'b'])
network = deep_cfr.DeepRegretNetwork(state_shape=(5, ),
action_indexer=action_indexer,
player=1)
with tf.Session() as sess:
network.set_sess(sess)
network.initialise()
computed = network.predict_advantages(info_set_vector,
action_indexer)
self.assertEqual(set(computed.keys()), {'a', 'b'})
self.assertTrue(type(computed['a']) == np.float32)
self.assertTrue(type(computed['b']) == np.float32)
def create_neural_rock_paper_scissors() -> typing.Tuple[extensive_game.ExtensiveGame,
neural_game.ActionIndexer,
neural_game.InfoSetVectoriser]:
"""Returns a rock paper scissors game, ActionIndexer and InfoStateVectoriser.
Returns:
NeuralGame
"""
game = create_rock_paper_scissors()
action_indexer = neural_game.ActionIndexer(['R', 'P', 'S'])
# There are only two information sets in rock, paper, scissors. Each player has one, since each player knows no
# information about the other player's move when they take their own move.
vectors = {
game.get_info_set_id(game.root): np.array([1, 0]),
game.get_info_set_id(game.root.children['R']): np.array([0, 1]),
}
info_set_vectoriser = neural_game.InfoSetVectoriser(vectors)
return neural_game.NeuralGame(game, action_indexer, info_set_vectoriser)
def test_get_dim(self):
action_indexer = neural_game.ActionIndexer(['a', 'b', 'c'])
self.assertEqual(action_indexer.action_dim, 3)
action_indexer = neural_game.ActionIndexer(['a', 'b', 'c', 'd', 'e'])
self.assertEqual(action_indexer.action_dim, 5)