class NetworkTest(unittest.TestCase):
def setUp(self):
# Make tests reproducible.
np.random.seed(0)
tf.random.set_seed(0)
self.env = TicTacToeEnv()
self.network_initializer = TicTacToeInitializer()
self.network = Network(self.network_initializer)
self.model = MuZeroMctsModel(self.env, self.network)
def test_initial_inference(self):
game_state = np.array(self.env.get_states()).reshape(
-1, TicTacToeConfig.action_size)
output = self.network.initial_inference(game_state)
self.assertTrue(output.reward == 0)
# self.assertTrue(output.value.shape == (1, 2*TicTacToeConfig.support_size + 1))
self.assertTrue(output.value.shape == (1, TicTacToeConfig.value_size))
self.assertTrue(
output.policy_logits.shape == (1, TicTacToeConfig.action_size))
self.assertTrue(
output.hidden_state.shape == (1, TicTacToeConfig.hidden_size))
def test_recurrent_inference(self):
game_state = np.array(self.env.get_states()).reshape(
-1, TicTacToeConfig.action_size)
action = Action(0)
output = self.network.recurrent_inference(game_state, action)
#self.assertEqual(0, output.reward)
# self.assertTrue(output.value.shape == (1, 2*TicTacToeConfig.support_size + 1))
self.assertTrue(output.value.shape == (1, TicTacToeConfig.value_size))
self.assertTrue(
output.policy_logits.shape == (1, TicTacToeConfig.action_size))
self.assertTrue(
output.hidden_state.shape == (1, TicTacToeConfig.hidden_size))
class TicTacToeNetworkInitializerTest(unittest.TestCase):
def setUp(self):
# Make tests reproducible.
np.random.seed(0)
tf.random.set_seed(0)
self.env = TicTacToeEnv()
self.network_initializer = TicTacToeInitializer()
self.prediction_network, self.dynamics_network, self.representation_network, self.dynamics_encoder, self.representation_encoder = self.network_initializer.initialize(
)
def test_prediction_network(self):
input_image = np.array(self.env.get_states()).reshape(
-1, TicTacToeConfig.action_size)
policy_logits, value = self.prediction_network(input_image)
# self.assertTrue(value.shape == (1, 2*TicTacToeConfig.support_size + 1))
self.assertTrue(value.shape == (1, TicTacToeConfig.value_size))
self.assertTrue(policy_logits.shape == (1,
TicTacToeConfig.action_size))
def test_representation_network(self):
input_image = np.array(self.env.get_states()).reshape(
-1, TicTacToeConfig.action_size)
hidden_state = self.representation_network(input_image)
self.assertEqual([1, TicTacToeConfig.hidden_size], hidden_state.shape)
print('LAYERS: ', self.representation_network.layers)
print('INPUTS: ', self.representation_network.inputs)
print('OUTPUTS: ', self.representation_network.outputs)
print('SUMMARY: ', self.representation_network.summary())
# self.assertEqual(output.value, np.zeros([1, 2*support_size + 1]))
# self.assertTrue(output.reward == 0)
# self.assertTrue(output.reward == 0)
def test_dynamics_network(self):
hidden_state = np.zeros(
(TicTacToeConfig.batch_size, TicTacToeConfig.hidden_size))
action = Action(0)
encoded_state = self.dynamics_encoder.encode(hidden_state, action)
hidden_state, reward = self.dynamics_network(encoded_state)
#self.assertTrue(reward == 0)
self.assertTrue(hidden_state.shape == (1, TicTacToeConfig.hidden_size))
def test_encoded_dynamics_state(self):
# hidden_state = np.zeros((TicTacToeConfig.batch_size, TicTacToeConfig.hidden_size))
hidden_state = np.zeros(TicTacToeConfig.hidden_size)
action = Action(0)
hidden_state = np.zeros(
(TicTacToeConfig.batch_size, TicTacToeConfig.hidden_size))
encoded_state = self.dynamics_encoder.encode(hidden_state, action)
#TODO(FJUR): This should encode 2 planes, a 1 hot plane with the selected action,
# and a binary plane whether the move was valid or not (all 0's or 1's)
self.assertTrue(
encoded_state.shape == (1, TicTacToeConfig.representation_size))
class BasicMctsModelTest(unittest.TestCase):
def setUp(self):
self.env = TicTacToeEnv()
self.dynamics_model = BasicMctsModel(self.env)
def test_get_predicted_value_and_final_info_discounted(self):
self.dynamics_model = BasicMctsModel(self.env, discount=0.9)
# Check some conditions first.
states = [0] * 9
states[4] = 1
states[0] = 4
self.assertEqual((False, 0.0), self.env.check(states))
self.assertEqual(1, self.env.opponent_play(states))
predicted_value, final_info = self.dynamics_model.get_predicted_value_and_final_info(
states)
self.assertEqual(-.9, predicted_value)
# Game ended for an illegal move.
self.assertEqual([4, 4, 0, 0, 1, 1, 0, 0, 0], final_info[0]) # states
self.assertEqual(5, final_info[1]) # action
def test_get_predicted_value_and_final_info(self):
# Check some conditions first.
states = [0] * 9
states[4] = 1
states[0] = 4
self.assertEqual((False, 0.0), self.env.check(states))
self.assertEqual(1, self.env.opponent_play(states))
predicted_value, final_info = self.dynamics_model.get_predicted_value_and_final_info(
states)
self.assertEqual(-1., predicted_value)
# Game ended for an illegal move.
self.assertEqual([4, 4, 0, 0, 1, 1, 0, 0, 0], final_info[0]) # states
self.assertEqual(5, final_info[1]) # action
def test_step(self):
self.dynamics_model.step([0] * 9, 0)
# The above is a simulation step, so it should not affect the real environment.
self.assertEqual([0] * 9, self.env.get_states())
class MctsCoreTicTacToeTest(unittest.TestCase):
def setUp(self):
self.env = TicTacToeEnv()
self.model = BasicMctsModel(self.env)
self.core = MctsCore(env=self.env, model=self.model)
def test_rollout(self):
self.core.initialize()
# TODO: test more succinctly.
self.assertEqual('{v: 0, p: 1.0, v_sum: 0, s: [0, 0, 0, 0, 0, 0, 0, 0, 0], r: 0, c: {0: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 1: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 2: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 3: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 4: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 5: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 6: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 7: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 8: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}}}',
str(self.core.get_root_for_testing()))
self.core.rollout()
# TODO: test more succinctly.
self.assertEqual('{v: 1, p: 1.0, v_sum: -1.0, s: [0, 0, 0, 0, 0, 0, 0, 0, 0], r: 0, c: {0: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 1: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 2: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 3: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 4: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 5: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 6: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 7: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 8: {v: 1, p: 1.0, v_sum: -1.0, s: [4, 0, 0, 0, 0, 0, 0, 0, 1], r: 0.0, c: {0: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 1: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 2: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 3: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 4: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 5: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 6: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 7: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}, 8: {v: 0, p: 1.0, v_sum: 0, s: None, r: 0, c: {}}}}}}',
str(self.core.get_root_for_testing()))
# rollout should not affect the actual test.
self.assertEqual([0] * 9, self.env.get_states())
self.core.rollout()
# TODO: check nodes after rollout
def get_ucb_distribution(self, node):
# A list of (ucb, action, child).
l1 = list(self.core.get_ucb_distribution(node))
l2 = [(action, ucb) for ucb, action, child in l1]
l3 = sorted(l2)
# Returns the list of ucb in the order of action.
return [ucb for _, ucb in l3]
def test_inside_initial_rollouts(self):
self.core.initialize()
root = self.core.get_root_for_testing()
self.assertEqual([0.] * 9, self.get_ucb_distribution(root))
node1, search_path, last_action = self.core.select_node()
self.assertNotEqual(root, node1)
self.assertEqual([root, node1], search_path)
# We can choose any action since the ucb distribution is uniform over actions.
self.assertEqual(8, last_action)
self.core.expand_node(node1)
self.assertTrue(node1.expanded())
parent = root
self.assertIsNotNone(parent.states)
value = self.core.evaluate_node(node1, parent.states, last_action)
self.assertEqual(0., node1.reward)
# Opponent (4) placed X at the first empty space.
self.assertEqual([4] + [0] * 7 + [1], node1.states)
# It is likely to lose the game when simulating this.
self.assertEqual(-1., value)
self.assertFalse(node1.is_final)
self.core.backpropagate(search_path, value)
# Action of 8 yielded a reward of 0. The action has been discounted.
# TODO: verify that the numbers are correct.
np.testing.assert_almost_equal([1.2501018] * 8 + [-0.3749491],
self.get_ucb_distribution(root))
# We visited only action 8. The result is somewhat counter-intuitive so
# far, but the policy is 100% on action 8.
np.testing.assert_array_equal([0.] * 8 + [1.],
self.core.get_policy_distribution())
