def test_action_generator():
# Continuous
action_len = 30
specs = BehaviorSpec(
observation_shapes=[(5, )],
action_type=ActionType.CONTINUOUS,
action_shape=action_len,
)
zero_action = specs.create_empty_action(4)
assert np.array_equal(zero_action,
np.zeros((4, action_len), dtype=np.float32))
random_action = specs.create_random_action(4)
assert random_action.dtype == np.float32
assert random_action.shape == (4, action_len)
assert np.min(random_action) >= -1
assert np.max(random_action) <= 1
# Discrete
action_shape = (10, 20, 30)
specs = BehaviorSpec(
observation_shapes=[(5, )],
action_type=ActionType.DISCRETE,
action_shape=action_shape,
)
zero_action = specs.create_empty_action(4)
assert np.array_equal(zero_action,
np.zeros((4, len(action_shape)), dtype=np.int32))
random_action = specs.create_random_action(4)
assert random_action.dtype == np.int32
assert random_action.shape == (4, len(action_shape))
assert np.min(random_action) >= 0
for index, branch_size in enumerate(action_shape):
assert np.max(random_action[:, index]) < branch_size
def test_specs():
specs = BehaviorSpec(
observation_shapes=[(3, 2), (5, )],
action_type=ActionType.CONTINUOUS,
action_shape=3,
)
assert specs.discrete_action_branches is None
assert specs.action_size == 3
assert specs.create_empty_action(5).shape == (5, 3)
assert specs.create_empty_action(5).dtype == np.float32
specs = BehaviorSpec(
observation_shapes=[(3, 2), (5, )],
action_type=ActionType.DISCRETE,
action_shape=(3, ),
)
assert specs.discrete_action_branches == (3, )
assert specs.action_size == 1
assert specs.create_empty_action(5).shape == (5, 1)
assert specs.create_empty_action(5).dtype == np.int32
