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 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 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 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)
