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 create_agent_buffer(behavior_spec: BehaviorSpec,
number: int,
reward: float = 0.0) -> AgentBuffer:
buffer = AgentBuffer()
curr_observations = [
np.random.normal(size=shape)
for shape in behavior_spec.observation_shapes
]
next_observations = [
np.random.normal(size=shape)
for shape in behavior_spec.observation_shapes
]
action = behavior_spec.create_random_action(1)[0, :]
for _ in range(number):
curr_split_obs = SplitObservations.from_observations(curr_observations)
next_split_obs = SplitObservations.from_observations(next_observations)
for i, _ in enumerate(curr_split_obs.visual_observations):
buffer["visual_obs%d" % i].append(
curr_split_obs.visual_observations[i])
buffer["next_visual_obs%d" % i].append(
next_split_obs.visual_observations[i])
buffer["vector_obs"].append(curr_split_obs.vector_observations)
buffer["next_vector_in"].append(next_split_obs.vector_observations)
buffer["actions"].append(action)
buffer["done"].append(np.zeros(1, dtype=np.float32))
buffer["reward"].append(np.ones(1, dtype=np.float32) * reward)
buffer["masks"].append(np.ones(1, dtype=np.float32))
return buffer
