def test_batched_step_result_from_proto_raises_on_nan():
n_agents = 10
shapes = [(3,), (4,)]
group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 3)
ap_list = generate_list_agent_proto(n_agents, shapes, nan_observations=True)
with pytest.raises(RuntimeError):
batched_step_result_from_proto(ap_list, group_spec)
def create_mock_group_spec(
number_visual_observations=0,
vector_action_space_type="continuous",
vector_observation_space_size=3,
vector_action_space_size=None,
):
"""
Creates a mock BrainParameters object with parameters.
"""
# Avoid using mutable object as default param
act_type = ActionType.DISCRETE
if vector_action_space_type == "continuous":
act_type = ActionType.CONTINUOUS
if vector_action_space_size is None:
vector_action_space_size = 2
else:
vector_action_space_size = vector_action_space_size[0]
else:
if vector_action_space_size is None:
vector_action_space_size = (2, )
else:
vector_action_space_size = tuple(vector_action_space_size)
obs_shapes = [(vector_observation_space_size, )]
for _ in range(number_visual_observations):
obs_shapes += [(8, 8, 3)]
return AgentGroupSpec(obs_shapes, act_type, vector_action_space_size)
def test_take_action_returns_empty_with_no_agents():
test_seed = 3
policy = FakePolicy(test_seed, basic_mock_brain(), basic_params())
# Doesn't really matter what this is
dummy_groupspec = AgentGroupSpec([(1, )], "continuous", 1)
no_agent_step = BatchedStepResult.empty(dummy_groupspec)
result = policy.get_action(no_agent_step)
assert result == ActionInfo.empty()
def test_action_masking_continuous():
n_agents = 10
shapes = [(3, ), (4, )]
group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 10)
ap_list = generate_list_agent_proto(n_agents, shapes)
result = batched_step_result_from_proto(ap_list, group_spec)
masks = result.action_mask
assert masks is None
def batched_step_result_from_proto(
agent_info_list: Collection[AgentInfoProto], # pylint: disable=unsubscriptable-object
group_spec: AgentGroupSpec,
) -> BatchedStepResult:
obs_list: List[np.ndarray] = []
for obs_index, obs_shape in enumerate(group_spec.observation_shapes):
is_visual = len(obs_shape) == 3
if is_visual:
obs_shape = cast(Tuple[int, int, int], obs_shape)
obs_list += [
_process_visual_observation(obs_index, obs_shape,
agent_info_list)
]
else:
obs_list += [
_process_vector_observation(obs_index, obs_shape,
agent_info_list)
]
rewards = np.array([agent_info.reward for agent_info in agent_info_list],
dtype=np.float32)
d = np.dot(rewards, rewards)
has_nan = np.isnan(d)
has_inf = not np.isfinite(d)
# In we have any NaN or Infs, use np.nan_to_num to replace these with finite values
if has_nan or has_inf:
rewards = np.nan_to_num(rewards)
if has_nan:
logger.warning(f"An agent had a NaN reward in the environment")
done = np.array([agent_info.done for agent_info in agent_info_list],
dtype=np.bool)
max_step = np.array(
[agent_info.max_step_reached for agent_info in agent_info_list],
dtype=np.bool)
agent_id = np.array([agent_info.id for agent_info in agent_info_list],
dtype=np.int32)
action_mask = None
if group_spec.is_action_discrete():
if any([agent_info.action_mask is not None]
for agent_info in agent_info_list):
n_agents = len(agent_info_list)
a_size = np.sum(group_spec.discrete_action_branches)
mask_matrix = np.ones((n_agents, a_size), dtype=np.bool)
for agent_index, agent_info in enumerate(agent_info_list):
if agent_info.action_mask is not None:
if len(agent_info.action_mask) == a_size:
mask_matrix[agent_index, :] = [
False if agent_info.action_mask[k] else True
for k in range(a_size)
]
action_mask = (1 - mask_matrix).astype(np.bool)
indices = _generate_split_indices(
group_spec.discrete_action_branches)
action_mask = np.split(action_mask, indices, axis=1)
return BatchedStepResult(obs_list, rewards, done, max_step, agent_id,
action_mask)
def test_action_masking_discrete_1():
n_agents = 10
shapes = [(3, ), (4, )]
group_spec = AgentGroupSpec(shapes, ActionType.DISCRETE, (10, ))
ap_list = generate_list_agent_proto(n_agents, shapes)
result = batched_step_result_from_proto(ap_list, group_spec)
masks = result.action_mask
assert isinstance(masks, list)
assert len(masks) == 1
assert masks[0].shape == (n_agents, 10)
assert masks[0][0, 0]
def batched_step_result_from_proto(
agent_info_list: Collection[AgentInfoProto], # pylint: disable=unsubscriptable-object
envStat: EnvironmentStatisticsProto,
group_spec: AgentGroupSpec,
) -> BatchedStepResult:
obs_list: List[np.ndarray] = []
for obs_index, obs_shape in enumerate(group_spec.observation_shapes):
is_visual = len(obs_shape) == 3
if is_visual:
obs_shape = cast(Tuple[int, int, int], obs_shape)
obs_list.append(
_process_visual_observation(obs_index, obs_shape,
agent_info_list))
else:
obs_list.append(
_process_vector_observation(obs_index, obs_shape,
agent_info_list))
rewards = np.array([agent_info.reward for agent_info in agent_info_list],
dtype=np.float32)
_raise_on_nan_and_inf(rewards, "rewards")
done = np.array([agent_info.done for agent_info in agent_info_list],
dtype=np.bool)
max_step = np.array(
[agent_info.max_step_reached for agent_info in agent_info_list],
dtype=np.bool)
agent_id = np.array([agent_info.id for agent_info in agent_info_list],
dtype=np.int32)
action_mask = None
if group_spec.is_action_discrete():
if any([agent_info.action_mask is not None]
for agent_info in agent_info_list):
n_agents = len(agent_info_list)
a_size = np.sum(group_spec.discrete_action_branches)
mask_matrix = np.ones((n_agents, a_size), dtype=np.bool)
for agent_index, agent_info in enumerate(agent_info_list):
if agent_info.action_mask is not None:
if len(agent_info.action_mask) == a_size:
mask_matrix[agent_index, :] = [
False if agent_info.action_mask[k] else True
for k in range(a_size)
]
action_mask = (1 - mask_matrix).astype(np.bool)
indices = _generate_split_indices(
group_spec.discrete_action_branches)
action_mask = np.split(action_mask, indices, axis=1)
# convert protobuf maps to dicts
double_stat = dict(
(key, envStat.double_stat[key]) for key in envStat.double_stat)
string_stat = dict(
(key, envStat.string_stat[key]) for key in envStat.string_stat)
return BatchedStepResult(obs_list, rewards, done, max_step, agent_id,
action_mask, double_stat, string_stat)
def __init__(self, use_discrete):
super().__init__()
self.discrete = use_discrete
action_type = ActionType.DISCRETE if use_discrete else ActionType.CONTINUOUS
self.group_spec = AgentGroupSpec([(OBS_SIZE, )], action_type,
(2, ) if use_discrete else 1)
# state
self.position = 0.0
self.step_count = 0
self.random = random.Random(str(self.group_spec))
self.goal = self.random.choice([-1, 1])
self.action = None
self.step_result = None
def test_batched_step_result_from_proto():
n_agents = 10
shapes = [(3, ), (4, )]
group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 3)
ap_list = generate_list_agent_proto(n_agents, shapes)
result = batched_step_result_from_proto(ap_list, group_spec)
assert list(result.reward) == list(range(n_agents))
assert list(result.agent_id) == list(range(n_agents))
for index in range(n_agents):
assert result.done[index] == (index % 2 == 0)
assert result.max_step[index] == (index % 2 == 1)
assert list(result.obs[0].shape) == [n_agents] + list(shapes[0])
assert list(result.obs[1].shape) == [n_agents] + list(shapes[1])
def __init__(
self,
brain_names,
use_discrete,
step_size=STEP_SIZE,
num_visual=0,
num_vector=1,
vis_obs_size=VIS_OBS_SIZE,
vec_obs_size=OBS_SIZE,
action_size=1,
):
super().__init__()
self.discrete = use_discrete
self.num_visual = num_visual
self.num_vector = num_vector
self.vis_obs_size = vis_obs_size
self.vec_obs_size = vec_obs_size
action_type = ActionType.DISCRETE if use_discrete else ActionType.CONTINUOUS
self.group_spec = AgentGroupSpec(
self._make_obs_spec(),
action_type,
tuple(
2
for _ in range(action_size)) if use_discrete else action_size,
)
self.action_size = action_size
self.names = brain_names
self.positions: Dict[str, List[float]] = {}
self.step_count: Dict[str, float] = {}
self.random = random.Random(str(self.group_spec))
self.goal: Dict[str, int] = {}
self.action = {}
self.rewards: Dict[str, float] = {}
self.final_rewards: Dict[str, List[float]] = {}
self.step_result: Dict[str, BatchedStepResult] = {}
self.agent_id: Dict[str, int] = {}
self.step_size = step_size # defines the difficulty of the test
for name in self.names:
self.agent_id[name] = 0
self.goal[name] = self.random.choice([-1, 1])
self.rewards[name] = 0
self.final_rewards[name] = []
self._reset_agent(name)
self.action[name] = None
self.step_result[name] = None
def step_result_to_brain_info(
step_result: BatchedStepResult,
group_spec: AgentGroupSpec,
agent_id_prefix: int = None,
) -> BrainInfo:
n_agents = step_result.n_agents()
vis_obs_indices = []
vec_obs_indices = []
for index, observation in enumerate(step_result.obs):
if len(observation.shape) == 2:
vec_obs_indices.append(index)
elif len(observation.shape) == 4:
vis_obs_indices.append(index)
else:
raise UnityEnvironmentException(
"Invalid input received from the environment, the observation should "
"either be a vector of float or a PNG image")
if len(vec_obs_indices) == 0:
vec_obs = np.zeros((n_agents, 0), dtype=np.float32)
else:
vec_obs = np.concatenate([step_result.obs[i] for i in vec_obs_indices],
axis=1)
vis_obs = [step_result.obs[i] for i in vis_obs_indices]
mask = np.ones((n_agents, np.sum(group_spec.action_size)),
dtype=np.float32)
if group_spec.is_action_discrete():
mask = np.ones((n_agents, np.sum(group_spec.discrete_action_branches)),
dtype=np.float32)
if step_result.action_mask is not None:
mask = 1 - np.concatenate(step_result.action_mask, axis=1)
if agent_id_prefix is None:
agent_ids = [str(ag_id) for ag_id in list(step_result.agent_id)]
else:
agent_ids = [
f"${agent_id_prefix}-{ag_id}" for ag_id in step_result.agent_id
]
return BrainInfo(
vis_obs,
vec_obs,
list(step_result.reward),
agent_ids,
list(step_result.done),
list(step_result.max_step),
mask,
)
def agent_group_spec_from_proto(brain_param_proto: BrainParametersProto,
agent_info: AgentInfoProto) -> AgentGroupSpec:
"""
Converts brain parameter and agent info proto to AgentGroupSpec object.
:param brain_param_proto: protobuf object.
:param agent_info: protobuf object.
:return: AgentGroupSpec object.
"""
observation_shape = [tuple(obs.shape) for obs in agent_info.observations]
action_type = (ActionType.DISCRETE
if brain_param_proto.vector_action_space_type == 0 else
ActionType.CONTINUOUS)
if action_type == ActionType.CONTINUOUS:
action_shape: Union[int, Tuple[
int, ...]] = brain_param_proto.vector_action_size[0]
else:
action_shape = tuple(brain_param_proto.vector_action_size)
return AgentGroupSpec(observation_shape, action_type, action_shape)
def group_spec_to_brain_parameters(
name: str, group_spec: AgentGroupSpec) -> BrainParameters:
vec_size = np.sum([
shape[0] for shape in group_spec.observation_shapes if len(shape) == 1
])
vis_sizes = [
shape for shape in group_spec.observation_shapes if len(shape) == 3
]
cam_res = [CameraResolution(s[0], s[1], s[2]) for s in vis_sizes]
a_size: List[int] = []
if group_spec.is_action_discrete():
a_size += list(group_spec.discrete_action_branches)
vector_action_space_type = 0
else:
a_size += [group_spec.action_size]
vector_action_space_type = 1
return BrainParameters(name, int(vec_size), cam_res, a_size, [],
vector_action_space_type)
def __init__(self, brain_names, use_discrete):
super().__init__()
self.discrete = use_discrete
action_type = ActionType.DISCRETE if use_discrete else ActionType.CONTINUOUS
self.group_spec = AgentGroupSpec([(OBS_SIZE, )], action_type,
(2, ) if use_discrete else 1)
self.names = brain_names
self.position: Dict[str, float] = {}
self.step_count: Dict[str, float] = {}
self.random = random.Random(str(self.group_spec))
self.goal: Dict[str, int] = {}
self.action = {}
self.rewards: Dict[str, float] = {}
self.final_rewards: Dict[str, List[float]] = {}
self.step_result: Dict[str, BatchedStepResult] = {}
for name in self.names:
self.goal[name] = self.random.choice([-1, 1])
self.rewards[name] = 0
self.final_rewards[name] = []
self._reset_agent(name)
self.action[name] = None
self.step_result[name] = None