def test_game_decrease(game):
# Create "mock" state that ends after 20 moves with the learner losing
lose_state = [MockState(go.WHITE, 20, size=19)]
policy1 = CNNPolicy.load_model(
os.path.join('tests', 'test_data', 'minimodel_policy.json'))
policy2 = CNNPolicy.load_model(
os.path.join('tests', 'test_data', 'minimodel_policy.json'))
learner = MockPlayer(policy1, game)
opponent = MockPlayer(policy2, game)
optimizer = SGD(lr=0.001)
policy1.model.compile(loss=log_loss, optimizer=optimizer)
# Get initial (before learning) move probabilities for all moves made by black
init_move_probs = get_sgf_move_probs(game, policy1, go.BLACK)
init_probs = [prob for (mv, prob) in init_move_probs]
# Run RL training
run_n_games(optimizer,
0.001,
learner,
opponent,
1,
mock_states=lose_state)
# Get new move probabilities for black's moves having finished 1 round of training
new_move_probs = get_sgf_move_probs(game, policy1, go.BLACK)
new_probs = [prob for (mv, prob) in new_move_probs]
# Assert that, on average, move probabilities for black decreased having lost.
self.assertTrue(
sum((new_probs[i] - init_probs[i]) for i in range(10)) < 0)
def run_and_get_new_weights(init_weights, winners, game):
# Create "mock" states that end after 2 moves with a predetermined winner.
states = [MockState(winner, 2, size=19) for winner in winners]
policy1 = CNNPolicy.load_model(
os.path.join('tests', 'test_data', 'minimodel_policy.json'))
policy2 = CNNPolicy.load_model(
os.path.join('tests', 'test_data', 'minimodel_policy.json'))
policy1.model.set_weights(init_weights)
optimizer = SGD(lr=0.001)
policy1.model.compile(loss=log_loss, optimizer=optimizer)
learner = MockPlayer(policy1, game)
opponent = MockPlayer(policy2, game)
# Run RL training
run_n_games(optimizer,
0.001,
learner,
opponent,
2,
mock_states=states)
return policy1.model.get_weights()
def test_game_run_N(game):
policy1 = CNNPolicy.load_model(os.path.join('tests', 'test_data', 'minimodel.json'))
policy2 = CNNPolicy.load_model(os.path.join('tests', 'test_data', 'minimodel.json'))
learner = MockPlayer(policy1, game)
opponent = MockPlayer(policy2, game)
optimizer = SGD()
init_weights = policy1.model.get_weights()
policy1.model.compile(loss=log_loss, optimizer=optimizer)
# Run RL training
run_n_games(optimizer, learner, opponent, 2)
# Get new weights for comparison
trained_weights = policy1.model.get_weights()
# Assert that some parameters changed.
any_change = any(not np.array_equal(i, t)
for (i, t) in zip(init_weights, trained_weights))
self.assertTrue(any_change)