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273 行
10 KiB

import logging
import numpy as np
import io
from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto
from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
from mlagents.envs.timers import hierarchical_timer, timed
from typing import Dict, List, NamedTuple, Optional
from PIL import Image
logger = logging.getLogger("mlagents.envs")
class CameraResolution(NamedTuple):
height: int
width: int
num_channels: int
@property
def gray_scale(self) -> bool:
return self.num_channels == 1
class BrainParameters:
def __init__(
self,
brain_name: str,
vector_observation_space_size: int,
num_stacked_vector_observations: int,
camera_resolutions: List[CameraResolution],
vector_action_space_size: List[int],
vector_action_descriptions: List[str],
vector_action_space_type: int,
):
"""
Contains all brain-specific parameters.
"""
self.brain_name = brain_name
self.vector_observation_space_size = vector_observation_space_size
self.num_stacked_vector_observations = num_stacked_vector_observations
self.number_visual_observations = len(camera_resolutions)
self.camera_resolutions = camera_resolutions
self.vector_action_space_size = vector_action_space_size
self.vector_action_descriptions = vector_action_descriptions
self.vector_action_space_type = ["discrete", "continuous"][
vector_action_space_type
]
def __str__(self):
return """Unity brain name: {}
Number of Visual Observations (per agent): {}
Vector Observation space size (per agent): {}
Number of stacked Vector Observation: {}
Vector Action space type: {}
Vector Action space size (per agent): {}
Vector Action descriptions: {}""".format(
self.brain_name,
str(self.number_visual_observations),
str(self.vector_observation_space_size),
str(self.num_stacked_vector_observations),
self.vector_action_space_type,
str(self.vector_action_space_size),
", ".join(self.vector_action_descriptions),
)
@staticmethod
def from_proto(
brain_param_proto: BrainParametersProto, agent_info: AgentInfoProto
) -> "BrainParameters":
"""
Converts brain parameter proto to BrainParameter object.
:param brain_param_proto: protobuf object.
:return: BrainParameter object.
"""
resolutions = [
CameraResolution(x.shape[0], x.shape[1], x.shape[2])
for x in agent_info.compressed_observations
]
brain_params = BrainParameters(
brain_param_proto.brain_name,
brain_param_proto.vector_observation_size,
brain_param_proto.num_stacked_vector_observations,
resolutions,
list(brain_param_proto.vector_action_size),
list(brain_param_proto.vector_action_descriptions),
brain_param_proto.vector_action_space_type,
)
return brain_params
class BrainInfo:
def __init__(
self,
visual_observation,
vector_observation,
text_observations,
reward=None,
agents=None,
local_done=None,
vector_action=None,
text_action=None,
max_reached=None,
action_mask=None,
custom_observations=None,
):
"""
Describes experience at current step of all agents linked to a brain.
"""
self.visual_observations = visual_observation
self.vector_observations = vector_observation
self.text_observations = text_observations
self.rewards = reward
self.local_done = local_done
self.max_reached = max_reached
self.agents = agents
self.previous_vector_actions = vector_action
self.previous_text_actions = text_action
self.action_masks = action_mask
self.custom_observations = custom_observations
@staticmethod
def merge_memories(m1, m2, agents1, agents2):
if len(m1) == 0 and len(m2) != 0:
m1 = np.zeros((len(agents1), m2.shape[1]))
elif len(m2) == 0 and len(m1) != 0:
m2 = np.zeros((len(agents2), m1.shape[1]))
elif m2.shape[1] > m1.shape[1]:
new_m1 = np.zeros((m1.shape[0], m2.shape[1]))
new_m1[0 : m1.shape[0], 0 : m1.shape[1]] = m1
return np.append(new_m1, m2, axis=0)
elif m1.shape[1] > m2.shape[1]:
new_m2 = np.zeros((m2.shape[0], m1.shape[1]))
new_m2[0 : m2.shape[0], 0 : m2.shape[1]] = m2
return np.append(m1, new_m2, axis=0)
return np.append(m1, m2, axis=0)
@staticmethod
@timed
def process_pixels(image_bytes: bytes, gray_scale: bool) -> np.ndarray:
"""
Converts byte array observation image into numpy array, re-sizes it,
and optionally converts it to grey scale
:param gray_scale: Whether to convert the image to grayscale.
:param image_bytes: input byte array corresponding to image
:return: processed numpy array of observation from environment
"""
with hierarchical_timer("image_decompress"):
image_bytearray = bytearray(image_bytes)
image = Image.open(io.BytesIO(image_bytearray))
# Normally Image loads lazily, this forces it to do loading in the timer scope.
image.load()
s = np.array(image) / 255.0
if gray_scale:
s = np.mean(s, axis=2)
s = np.reshape(s, [s.shape[0], s.shape[1], 1])
return s
@staticmethod
def from_agent_proto(
worker_id: int,
agent_info_list: List[AgentInfoProto],
brain_params: BrainParameters,
) -> "BrainInfo":
"""
Converts list of agent infos to BrainInfo.
"""
vis_obs: List[np.ndarray] = []
for i in range(brain_params.number_visual_observations):
obs = [
BrainInfo.process_pixels(
x.compressed_observations[i].data,
brain_params.camera_resolutions[i].gray_scale,
)
for x in agent_info_list
]
vis_obs += [obs]
total_num_actions = sum(brain_params.vector_action_space_size)
mask_actions = np.ones((len(agent_info_list), total_num_actions))
for agent_index, agent_info in enumerate(agent_info_list):
if agent_info.action_mask is not None:
if len(agent_info.action_mask) == total_num_actions:
mask_actions[agent_index, :] = [
0 if agent_info.action_mask[k] else 1
for k in range(total_num_actions)
]
if any(np.isnan(x.reward) for x in agent_info_list):
logger.warning(
"An agent had a NaN reward for brain " + brain_params.brain_name
)
if len(agent_info_list) == 0:
vector_obs = np.zeros(
(
0,
brain_params.vector_observation_space_size
* brain_params.num_stacked_vector_observations,
)
)
else:
stacked_obs = []
has_nan = False
has_inf = False
for x in agent_info_list:
np_obs = np.array(x.stacked_vector_observation)
# Check for NaNs or infs in the observations
# If there's a NaN in the observations, the dot() result will be NaN
# If there's an Inf (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
# This is OK though, worst case it results in an unnecessary (but harmless) nan_to_num call.
d = np.dot(np_obs, np_obs)
has_nan = has_nan or np.isnan(d)
has_inf = has_inf or not np.isfinite(d)
stacked_obs.append(np_obs)
vector_obs = np.array(stacked_obs)
# In we have any NaN or Infs, use np.nan_to_num to replace these with finite values
if has_nan or has_inf:
vector_obs = np.nan_to_num(vector_obs)
if has_nan:
logger.warning(
f"An agent had a NaN observation for brain {brain_params.brain_name}"
)
agents = [f"${worker_id}-{x.id}" for x in agent_info_list]
brain_info = BrainInfo(
visual_observation=vis_obs,
vector_observation=vector_obs,
text_observations=[x.text_observation for x in agent_info_list],
reward=[x.reward if not np.isnan(x.reward) else 0 for x in agent_info_list],
agents=agents,
local_done=[x.done for x in agent_info_list],
vector_action=np.array([x.stored_vector_actions for x in agent_info_list]),
text_action=[list(x.stored_text_actions) for x in agent_info_list],
max_reached=[x.max_step_reached for x in agent_info_list],
custom_observations=[x.custom_observation for x in agent_info_list],
action_mask=mask_actions,
)
return brain_info
def safe_concat_lists(l1: Optional[List], l2: Optional[List]) -> Optional[List]:
if l1 is None:
if l2 is None:
return None
else:
return l2.copy()
else:
if l2 is None:
return l1.copy()
else:
copy = l1.copy()
copy.extend(l2)
return copy
def safe_concat_np_ndarray(
a1: Optional[np.ndarray], a2: Optional[np.ndarray]
) -> Optional[np.ndarray]:
if a1 is not None and a1.size != 0:
if a2 is not None and a2.size != 0:
return np.append(a1, a2, axis=0)
else:
return a1.copy()
elif a2 is not None and a2.size != 0:
return a2.copy()
return None
# Renaming of dictionary of brain name to BrainInfo for clarity
AllBrainInfo = Dict[str, BrainInfo]