Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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from mlagents_envs.base_env import (
ActionSpec,
ObservationSpec,
DimensionProperty,
BehaviorSpec,
DecisionSteps,
TerminalSteps,
ObservationType,
)
from mlagents_envs.exception import UnityObservationException
from mlagents_envs.timers import hierarchical_timer, timed
from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
from mlagents_envs.communicator_objects.observation_pb2 import (
ObservationProto,
NONE as COMPRESSION_TYPE_NONE,
)
from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
import numpy as np
import io
from typing import cast, List, Tuple, Collection, Optional, Iterable
from PIL import Image
PNG_HEADER = b"\x89PNG\r\n\x1a\n"
def behavior_spec_from_proto(
brain_param_proto: BrainParametersProto, agent_info: AgentInfoProto
) -> BehaviorSpec:
"""
Converts brain parameter and agent info proto to BehaviorSpec object.
:param brain_param_proto: protobuf object.
:param agent_info: protobuf object.
:return: BehaviorSpec object.
"""
observation_specs = []
for obs in agent_info.observations:
observation_specs.append(
ObservationSpec(
tuple(obs.shape),
tuple(DimensionProperty(dim) for dim in obs.dimension_properties)
if len(obs.dimension_properties) > 0
else (DimensionProperty.UNSPECIFIED,) * len(obs.shape),
ObservationType(obs.observation_type),
)
)
# proto from communicator < v1.3 does not set action spec, use deprecated fields instead
if (
brain_param_proto.action_spec.num_continuous_actions == 0
and brain_param_proto.action_spec.num_discrete_actions == 0
):
if brain_param_proto.vector_action_space_type_deprecated == 1:
action_spec = ActionSpec(
brain_param_proto.vector_action_size_deprecated[0], ()
)
else:
action_spec = ActionSpec(
0, tuple(brain_param_proto.vector_action_size_deprecated)
)
else:
action_spec_proto = brain_param_proto.action_spec
action_spec = ActionSpec(
action_spec_proto.num_continuous_actions,
tuple(branch for branch in action_spec_proto.discrete_branch_sizes),
)
return BehaviorSpec(observation_specs, action_spec)
class OffsetBytesIO:
"""
Simple file-like class that wraps a bytes, and allows moving its "start"
position in the bytes. This is only used for reading concatenated PNGs,
because Pillow always calls seek(0) at the start of reading.
"""
__slots__ = ["fp", "offset"]
def __init__(self, data: bytes):
self.fp = io.BytesIO(data)
self.offset = 0
def seek(self, offset: int, whence: int = io.SEEK_SET) -> int:
if whence == io.SEEK_SET:
res = self.fp.seek(offset + self.offset)
return res - self.offset
raise NotImplementedError()
def tell(self) -> int:
return self.fp.tell() - self.offset
def read(self, size: int = -1) -> bytes:
return self.fp.read(size)
def original_tell(self) -> int:
"""
Returns the offset into the original byte array
"""
return self.fp.tell()
@timed
def process_pixels(
image_bytes: bytes, expected_channels: int, mappings: Optional[List[int]] = None
) -> np.ndarray:
"""
Converts byte array observation image into numpy array, re-sizes it,
and optionally converts it to grey scale
:param image_bytes: input byte array corresponding to image
:param expected_channels: Expected output channels
:return: processed numpy array of observation from environment
"""
image_fp = OffsetBytesIO(image_bytes)
image_arrays = []
# Read the images back from the bytes (without knowing the sizes).
while True:
with hierarchical_timer("image_decompress"):
image = Image.open(image_fp)
# Normally Image loads lazily, load() forces it to do loading in the timer scope.
image.load()
image_arrays.append(np.array(image, dtype=np.float32) / 255.0)
# Look for the next header, starting from the current stream location
try:
new_offset = image_bytes.index(PNG_HEADER, image_fp.original_tell())
image_fp.offset = new_offset
except ValueError:
# Didn't find the header, so must be at the end.
break
if mappings is not None and len(mappings) > 0:
return _process_images_mapping(image_arrays, mappings)
else:
return _process_images_num_channels(image_arrays, expected_channels)
def _process_images_mapping(image_arrays, mappings):
"""
Helper function for processing decompressed images with compressed channel mappings.
"""
image_arrays = np.concatenate(image_arrays, axis=2).transpose((2, 0, 1))
if len(mappings) != len(image_arrays):
raise UnityObservationException(
f"Compressed observation and its mapping had different number of channels - "
f"observation had {len(image_arrays)} channels but its mapping had {len(mappings)} channels"
)
if len({m for m in mappings if m > -1}) != max(mappings) + 1:
raise UnityObservationException(
f"Invalid Compressed Channel Mapping: the mapping {mappings} does not have the correct format."
)
if max(mappings) >= len(image_arrays):
raise UnityObservationException(
f"Invalid Compressed Channel Mapping: the mapping has index larger than the total "
f"number of channels in observation - mapping index {max(mappings)} is"
f"invalid for input observation with {len(image_arrays)} channels."
)
processed_image_arrays: List[np.array] = [[] for _ in range(max(mappings) + 1)]
for mapping_idx, img in zip(mappings, image_arrays):
if mapping_idx > -1:
processed_image_arrays[mapping_idx].append(img)
for i, img_array in enumerate(processed_image_arrays):
processed_image_arrays[i] = np.mean(img_array, axis=0)
img = np.stack(processed_image_arrays, axis=2)
return img
def _process_images_num_channels(image_arrays, expected_channels):
"""
Helper function for processing decompressed images with number of expected channels.
This is for old API without mapping provided. Use the first n channel, n=expected_channels.
"""
if expected_channels == 1:
# Convert to grayscale
img = np.mean(image_arrays[0], axis=2)
img = np.reshape(img, [img.shape[0], img.shape[1], 1])
else:
img = np.concatenate(image_arrays, axis=2)
# We can drop additional channels since they may need to be added to include
# numbers of observation channels not divisible by 3.
actual_channels = list(img.shape)[2]
if actual_channels > expected_channels:
img = img[..., 0:expected_channels]
return img
@timed
def observation_to_np_array(
obs: ObservationProto, expected_shape: Optional[Iterable[int]] = None
) -> np.ndarray:
"""
Converts observation proto into numpy array of the appropriate size.
:param obs: observation proto to be converted
:param expected_shape: optional shape information, used for sanity checks.
:return: processed numpy array of observation from environment
"""
if expected_shape is not None:
if list(obs.shape) != list(expected_shape):
raise UnityObservationException(
f"Observation did not have the expected shape - got {obs.shape} but expected {expected_shape}"
)
expected_channels = obs.shape[2]
if obs.compression_type == COMPRESSION_TYPE_NONE:
img = np.array(obs.float_data.data, dtype=np.float32)
img = np.reshape(img, obs.shape)
return img
else:
img = process_pixels(
obs.compressed_data, expected_channels, list(obs.compressed_channel_mapping)
)
# Compare decompressed image size to observation shape and make sure they match
if list(obs.shape) != list(img.shape):
raise UnityObservationException(
f"Decompressed observation did not have the expected shape - "
f"decompressed had {img.shape} but expected {obs.shape}"
)
return img
@timed
def _process_visual_observation(
obs_index: int,
shape: Tuple[int, int, int],
agent_info_list: Collection[AgentInfoProto],
) -> np.ndarray:
if len(agent_info_list) == 0:
return np.zeros((0, shape[0], shape[1], shape[2]), dtype=np.float32)
batched_visual = [
observation_to_np_array(agent_obs.observations[obs_index], shape)
for agent_obs in agent_info_list
]
return np.array(batched_visual, dtype=np.float32)
def _raise_on_nan_and_inf(data: np.array, source: str) -> np.array:
# Check for NaNs or Infinite values in the observation or reward data.
# If there's a NaN in the observations, the np.mean() result will be NaN
# If there's an Infinite value (either sign) then the result will be Inf
# See https://stackoverflow.com/questions/6736590/fast-check-for-nan-in-numpy for background
# Note that a very large values (larger than sqrt(float_max)) will result in an Inf value here
# Raise a Runtime error in the case that NaNs or Infinite values make it into the data.
if data.size == 0:
return data
d = np.mean(data)
has_nan = np.isnan(d)
has_inf = not np.isfinite(d)
if has_nan:
raise RuntimeError(f"The {source} provided had NaN values.")
if has_inf:
raise RuntimeError(f"The {source} provided had Infinite values.")
@timed
def _process_vector_observation(
obs_index: int, shape: Tuple[int, ...], agent_info_list: Collection[AgentInfoProto]
) -> np.ndarray:
if len(agent_info_list) == 0:
return np.zeros((0,) + shape, dtype=np.float32)
np_obs = np.array(
[
agent_obs.observations[obs_index].float_data.data
for agent_obs in agent_info_list
],
dtype=np.float32,
).reshape((len(agent_info_list),) + shape)
_raise_on_nan_and_inf(np_obs, "observations")
return np_obs
@timed
def steps_from_proto(
agent_info_list: Collection[AgentInfoProto], behavior_spec: BehaviorSpec
) -> Tuple[DecisionSteps, TerminalSteps]:
decision_agent_info_list = [
agent_info for agent_info in agent_info_list if not agent_info.done
]
terminal_agent_info_list = [
agent_info for agent_info in agent_info_list if agent_info.done
]
decision_obs_list: List[np.ndarray] = []
terminal_obs_list: List[np.ndarray] = []
for obs_index, observation_specs in enumerate(behavior_spec.observation_specs):
is_visual = len(observation_specs.shape) == 3
if is_visual:
obs_shape = cast(Tuple[int, int, int], observation_specs.shape)
decision_obs_list.append(
_process_visual_observation(
obs_index, obs_shape, decision_agent_info_list
)
)
terminal_obs_list.append(
_process_visual_observation(
obs_index, obs_shape, terminal_agent_info_list
)
)
else:
decision_obs_list.append(
_process_vector_observation(
obs_index, observation_specs.shape, decision_agent_info_list
)
)
terminal_obs_list.append(
_process_vector_observation(
obs_index, observation_specs.shape, terminal_agent_info_list
)
)
decision_rewards = np.array(
[agent_info.reward for agent_info in decision_agent_info_list], dtype=np.float32
)
terminal_rewards = np.array(
[agent_info.reward for agent_info in terminal_agent_info_list], dtype=np.float32
)
_raise_on_nan_and_inf(decision_rewards, "rewards")
_raise_on_nan_and_inf(terminal_rewards, "rewards")
max_step = np.array(
[agent_info.max_step_reached for agent_info in terminal_agent_info_list],
dtype=np.bool,
)
decision_agent_id = np.array(
[agent_info.id for agent_info in decision_agent_info_list], dtype=np.int32
)
terminal_agent_id = np.array(
[agent_info.id for agent_info in terminal_agent_info_list], dtype=np.int32
)
action_mask = None
if behavior_spec.action_spec.discrete_size > 0:
if any(
[agent_info.action_mask is not None]
for agent_info in decision_agent_info_list
):
n_agents = len(decision_agent_info_list)
a_size = np.sum(behavior_spec.action_spec.discrete_branches)
mask_matrix = np.ones((n_agents, a_size), dtype=np.bool)
for agent_index, agent_info in enumerate(decision_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(
behavior_spec.action_spec.discrete_branches
)
action_mask = np.split(action_mask, indices, axis=1)
return (
DecisionSteps(
decision_obs_list, decision_rewards, decision_agent_id, action_mask
),
TerminalSteps(terminal_obs_list, terminal_rewards, max_step, terminal_agent_id),
)
def _generate_split_indices(dims):
if len(dims) <= 1:
return ()
result = (dims[0],)
for i in range(len(dims) - 2):
result += (dims[i + 1] + result[i],)
return result