def test_evaluate_leaf(self):
game_root = Game()
root = Node(game_root)
model = MagicMock()
prediction = [
np.array([[0.25]]),
np.reshape(np.arange(0.001, 0.897, step=0.001), newshape=(1, 896))
]
model.predict.return_value = prediction
action_encoder = ActionEncoder(DirectionResolver())
mcts = MCTS(root,
config={
'ALPHA': 0.8,
'CPUCT': 1,
'EPSILON': 0.2
},
model=model,
state_encoder=StateEncoder(),
action_encoder=action_encoder)
_, probs, _ = mcts.predict_state_value(game_root)
value = mcts.evaluate_leaf(root)
self.assertEqual(value, 0.25)
self.assertEqual(len(root.edges), 7)
self.assertEqual(root.edges[0].action, 8)
self.assertEqual(root.edges[0].stats['P'], probs[8])
self.assertEqual(root.edges[1].action, 104)
self.assertEqual(root.edges[1].stats['P'], probs[104])
def test_predict(self):
game_root = Game()
root = Node(game_root)
model = MagicMock()
prediction = [
np.array([[0.25]]),
np.reshape(np.arange(0.001, 0.897, step=0.001), newshape=(1, 896))
]
model.predict.return_value = prediction
action_encoder = ActionEncoder(DirectionResolver())
mcts = MCTS(root,
config={
'ALPHA': 0.8,
'CPUCT': 1,
'EPSILON': 0.2
},
model=model,
state_encoder=StateEncoder(),
action_encoder=action_encoder)
value, probs, allowed_actions = mcts.predict_state_value(game_root)
self.assertEqual(value, 0.25)
self.assertCountEqual(
allowed_actions,
action_encoder.convert_moves_to_action_ids(
game_root.get_possible_moves_from_current_player_perspective())
)
for idx, prob in enumerate(probs):
if idx in allowed_actions:
self.assertTrue(prob > 0.01)
else:
self.assertTrue(prob < np.exp(-40))
def test_integration(self):
HIDDEN_CNN_LAYERS = [{
'filters': 75,
'kernel_size': (4, 4)
}, {
'filters': 75,
'kernel_size': (4, 4)
}, {
'filters': 75,
'kernel_size': (4, 4)
}, {
'filters': 75,
'kernel_size': (4, 4)
}, {
'filters': 75,
'kernel_size': (4, 4)
}, {
'filters': 75,
'kernel_size': (4, 4)
}]
model = Residual_CNN(0.0001,
0.1, (2, 4, 8),
32 * 4,
HIDDEN_CNN_LAYERS,
momentum=0.9)
game_root = Game()
root = Node(game_root)
mcts = MCTS(root,
config={
'ALPHA': 0.8,
'CPUCT': 1,
'EPSILON': 0.2
},
model=model,
state_encoder=StateEncoder(),
action_encoder=ActionEncoder(DirectionResolver()))
mcts.predict_state_value(game_root)
mcts.evaluate_leaf(root)