您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
327 行
13 KiB
327 行
13 KiB
from typing import List, Optional, Tuple
|
|
from mlagents.torch_utils import torch, nn
|
|
import numpy as np
|
|
|
|
from mlagents.trainers.torch.encoders import (
|
|
SimpleVisualEncoder,
|
|
ResNetVisualEncoder,
|
|
NatureVisualEncoder,
|
|
SmallVisualEncoder,
|
|
VectorInput,
|
|
)
|
|
from mlagents.trainers.settings import EncoderType, ScheduleType
|
|
from mlagents.trainers.exception import UnityTrainerException
|
|
from mlagents_envs.base_env import ActionSpec
|
|
from mlagents.trainers.torch.distributions import DistInstance, DiscreteDistInstance
|
|
|
|
|
|
class ModelUtils:
|
|
# Minimum supported side for each encoder type. If refactoring an encoder, please
|
|
# adjust these also.
|
|
MIN_RESOLUTION_FOR_ENCODER = {
|
|
EncoderType.MATCH3: 5,
|
|
EncoderType.SIMPLE: 20,
|
|
EncoderType.NATURE_CNN: 36,
|
|
EncoderType.RESNET: 15,
|
|
}
|
|
|
|
class ActionFlattener:
|
|
def __init__(self, action_spec: ActionSpec):
|
|
self._specs = action_spec
|
|
|
|
@property
|
|
def flattened_size(self) -> int:
|
|
if self._specs.is_continuous():
|
|
return self._specs.continuous_size
|
|
else:
|
|
return sum(self._specs.discrete_branches)
|
|
|
|
def forward(self, action: torch.Tensor) -> torch.Tensor:
|
|
if self._specs.is_continuous():
|
|
return action
|
|
else:
|
|
return torch.cat(
|
|
ModelUtils.actions_to_onehot(
|
|
torch.as_tensor(action, dtype=torch.long),
|
|
self._specs.discrete_branches,
|
|
),
|
|
dim=1,
|
|
)
|
|
|
|
@staticmethod
|
|
def update_learning_rate(optim: torch.optim.Optimizer, lr: float) -> None:
|
|
"""
|
|
Apply a learning rate to a torch optimizer.
|
|
:param optim: Optimizer
|
|
:param lr: Learning rate
|
|
"""
|
|
for param_group in optim.param_groups:
|
|
param_group["lr"] = lr
|
|
|
|
class DecayedValue:
|
|
def __init__(
|
|
self,
|
|
schedule: ScheduleType,
|
|
initial_value: float,
|
|
min_value: float,
|
|
max_step: int,
|
|
):
|
|
"""
|
|
Object that represnets value of a parameter that should be decayed, assuming it is a function of
|
|
global_step.
|
|
:param schedule: Type of learning rate schedule.
|
|
:param initial_value: Initial value before decay.
|
|
:param min_value: Decay value to this value by max_step.
|
|
:param max_step: The final step count where the return value should equal min_value.
|
|
:param global_step: The current step count.
|
|
:return: The value.
|
|
"""
|
|
self.schedule = schedule
|
|
self.initial_value = initial_value
|
|
self.min_value = min_value
|
|
self.max_step = max_step
|
|
|
|
def get_value(self, global_step: int) -> float:
|
|
"""
|
|
Get the value at a given global step.
|
|
:param global_step: Step count.
|
|
:returns: Decayed value at this global step.
|
|
"""
|
|
if self.schedule == ScheduleType.CONSTANT:
|
|
return self.initial_value
|
|
elif self.schedule == ScheduleType.LINEAR:
|
|
return ModelUtils.polynomial_decay(
|
|
self.initial_value, self.min_value, self.max_step, global_step
|
|
)
|
|
else:
|
|
raise UnityTrainerException(f"The schedule {self.schedule} is invalid.")
|
|
|
|
@staticmethod
|
|
def polynomial_decay(
|
|
initial_value: float,
|
|
min_value: float,
|
|
max_step: int,
|
|
global_step: int,
|
|
power: float = 1.0,
|
|
) -> float:
|
|
"""
|
|
Get a decayed value based on a polynomial schedule, with respect to the current global step.
|
|
:param initial_value: Initial value before decay.
|
|
:param min_value: Decay value to this value by max_step.
|
|
:param max_step: The final step count where the return value should equal min_value.
|
|
:param global_step: The current step count.
|
|
:param power: Power of polynomial decay. 1.0 (default) is a linear decay.
|
|
:return: The current decayed value.
|
|
"""
|
|
global_step = min(global_step, max_step)
|
|
decayed_value = (initial_value - min_value) * (
|
|
1 - float(global_step) / max_step
|
|
) ** (power) + min_value
|
|
return decayed_value
|
|
|
|
@staticmethod
|
|
def get_encoder_for_type(encoder_type: EncoderType) -> nn.Module:
|
|
ENCODER_FUNCTION_BY_TYPE = {
|
|
EncoderType.SIMPLE: SimpleVisualEncoder,
|
|
EncoderType.NATURE_CNN: NatureVisualEncoder,
|
|
EncoderType.RESNET: ResNetVisualEncoder,
|
|
EncoderType.MATCH3: SmallVisualEncoder,
|
|
}
|
|
return ENCODER_FUNCTION_BY_TYPE.get(encoder_type)
|
|
|
|
@staticmethod
|
|
def _check_resolution_for_encoder(
|
|
height: int, width: int, vis_encoder_type: EncoderType
|
|
) -> None:
|
|
min_res = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[vis_encoder_type]
|
|
if height < min_res or width < min_res:
|
|
raise UnityTrainerException(
|
|
f"Visual observation resolution ({width}x{height}) is too small for"
|
|
f"the provided EncoderType ({vis_encoder_type.value}). The min dimension is {min_res}"
|
|
)
|
|
|
|
@staticmethod
|
|
def create_input_processors(
|
|
observation_shapes: List[Tuple[int, ...]],
|
|
h_size: int,
|
|
vis_encode_type: EncoderType,
|
|
normalize: bool = False,
|
|
) -> Tuple[nn.ModuleList, nn.ModuleList, int]:
|
|
"""
|
|
Creates visual and vector encoders, along with their normalizers.
|
|
:param observation_shapes: List of Tuples that represent the action dimensions.
|
|
:param action_size: Number of additional un-normalized inputs to each vector encoder. Used for
|
|
conditioining network on other values (e.g. actions for a Q function)
|
|
:param h_size: Number of hidden units per layer.
|
|
:param vis_encode_type: Type of visual encoder to use.
|
|
:param unnormalized_inputs: Vector inputs that should not be normalized, and added to the vector
|
|
obs.
|
|
:param normalize: Normalize all vector inputs.
|
|
:return: Tuple of visual encoders and vector encoders each as a list.
|
|
"""
|
|
visual_encoders: List[nn.Module] = []
|
|
vector_encoders: List[nn.Module] = []
|
|
|
|
visual_encoder_class = ModelUtils.get_encoder_for_type(vis_encode_type)
|
|
vector_size = 0
|
|
visual_output_size = 0
|
|
for i, dimension in enumerate(observation_shapes):
|
|
if len(dimension) == 3:
|
|
ModelUtils._check_resolution_for_encoder(
|
|
dimension[0], dimension[1], vis_encode_type
|
|
)
|
|
visual_encoders.append(
|
|
visual_encoder_class(
|
|
dimension[0], dimension[1], dimension[2], h_size
|
|
)
|
|
)
|
|
visual_output_size += h_size
|
|
elif len(dimension) == 1:
|
|
vector_size += dimension[0]
|
|
else:
|
|
raise UnityTrainerException(
|
|
f"Unsupported shape of {dimension} for observation {i}"
|
|
)
|
|
if vector_size > 0:
|
|
vector_encoders.append(VectorInput(vector_size, normalize))
|
|
# Total output size for all inputs + CNNs
|
|
total_processed_size = vector_size + visual_output_size
|
|
return (
|
|
nn.ModuleList(visual_encoders),
|
|
nn.ModuleList(vector_encoders),
|
|
total_processed_size,
|
|
)
|
|
|
|
@staticmethod
|
|
def list_to_tensor(
|
|
ndarray_list: List[np.ndarray], dtype: Optional[torch.dtype] = torch.float32
|
|
) -> torch.Tensor:
|
|
"""
|
|
Converts a list of numpy arrays into a tensor. MUCH faster than
|
|
calling as_tensor on the list directly.
|
|
"""
|
|
return torch.as_tensor(np.asanyarray(ndarray_list), dtype=dtype)
|
|
|
|
@staticmethod
|
|
def to_numpy(tensor: torch.Tensor) -> np.ndarray:
|
|
"""
|
|
Converts a Torch Tensor to a numpy array. If the Tensor is on the GPU, it will
|
|
be brought to the CPU.
|
|
"""
|
|
return tensor.detach().cpu().numpy()
|
|
|
|
@staticmethod
|
|
def break_into_branches(
|
|
concatenated_logits: torch.Tensor, action_size: List[int]
|
|
) -> List[torch.Tensor]:
|
|
"""
|
|
Takes a concatenated set of logits that represent multiple discrete action branches
|
|
and breaks it up into one Tensor per branch.
|
|
:param concatenated_logits: Tensor that represents the concatenated action branches
|
|
:param action_size: List of ints containing the number of possible actions for each branch.
|
|
:return: A List of Tensors containing one tensor per branch.
|
|
"""
|
|
action_idx = [0] + list(np.cumsum(action_size))
|
|
branched_logits = [
|
|
concatenated_logits[:, action_idx[i] : action_idx[i + 1]]
|
|
for i in range(len(action_size))
|
|
]
|
|
return branched_logits
|
|
|
|
@staticmethod
|
|
def actions_to_onehot(
|
|
discrete_actions: torch.Tensor, action_size: List[int]
|
|
) -> List[torch.Tensor]:
|
|
"""
|
|
Takes a tensor of discrete actions and turns it into a List of onehot encoding for each
|
|
action.
|
|
:param discrete_actions: Actions in integer form.
|
|
:param action_size: List of branch sizes. Should be of same size as discrete_actions'
|
|
last dimension.
|
|
:return: List of one-hot tensors, one representing each branch.
|
|
"""
|
|
onehot_branches = [
|
|
torch.nn.functional.one_hot(_act.T, action_size[i]).float()
|
|
for i, _act in enumerate(discrete_actions.long().T)
|
|
]
|
|
return onehot_branches
|
|
|
|
@staticmethod
|
|
def dynamic_partition(
|
|
data: torch.Tensor, partitions: torch.Tensor, num_partitions: int
|
|
) -> List[torch.Tensor]:
|
|
"""
|
|
Torch implementation of dynamic_partition :
|
|
https://www.tensorflow.org/api_docs/python/tf/dynamic_partition
|
|
Splits the data Tensor input into num_partitions Tensors according to the indices in
|
|
partitions.
|
|
:param data: The Tensor data that will be split into partitions.
|
|
:param partitions: An indices tensor that determines in which partition each element
|
|
of data will be in.
|
|
:param num_partitions: The number of partitions to output. Corresponds to the
|
|
maximum possible index in the partitions argument.
|
|
:return: A list of Tensor partitions (Their indices correspond to their partition index).
|
|
"""
|
|
res: List[torch.Tensor] = []
|
|
for i in range(num_partitions):
|
|
res += [data[(partitions == i).nonzero().squeeze(1)]]
|
|
return res
|
|
|
|
@staticmethod
|
|
def get_probs_and_entropy(
|
|
action_list: List[torch.Tensor], dists: List[DistInstance]
|
|
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
|
|
log_probs_list = []
|
|
all_probs_list = []
|
|
entropies_list = []
|
|
for action, action_dist in zip(action_list, dists):
|
|
log_prob = action_dist.log_prob(action)
|
|
log_probs_list.append(log_prob)
|
|
entropies_list.append(action_dist.entropy())
|
|
if isinstance(action_dist, DiscreteDistInstance):
|
|
all_probs_list.append(action_dist.all_log_prob())
|
|
log_probs = torch.stack(log_probs_list, dim=-1)
|
|
entropies = torch.stack(entropies_list, dim=-1)
|
|
if not all_probs_list:
|
|
log_probs = log_probs.squeeze(-1)
|
|
entropies = entropies.squeeze(-1)
|
|
all_probs = None
|
|
else:
|
|
all_probs = torch.cat(all_probs_list, dim=-1)
|
|
return log_probs, entropies, all_probs
|
|
|
|
@staticmethod
|
|
def masked_mean(tensor: torch.Tensor, masks: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Returns the mean of the tensor but ignoring the values specified by masks.
|
|
Used for masking out loss functions.
|
|
:param tensor: Tensor which needs mean computation.
|
|
:param masks: Boolean tensor of masks with same dimension as tensor.
|
|
"""
|
|
return (tensor.T * masks).sum() / torch.clamp(
|
|
(torch.ones_like(tensor.T) * masks).float().sum(), min=1.0
|
|
)
|
|
|
|
@staticmethod
|
|
def soft_update(source: nn.Module, target: nn.Module, tau: float) -> None:
|
|
"""
|
|
Performs an in-place polyak update of the target module based on the source,
|
|
by a ratio of tau. Note that source and target modules must have the same
|
|
parameters, where:
|
|
target = tau * source + (1-tau) * target
|
|
:param source: Source module whose parameters will be used.
|
|
:param target: Target module whose parameters will be updated.
|
|
:param tau: Percentage of source parameters to use in average. Setting tau to
|
|
1 will copy the source parameters to the target.
|
|
"""
|
|
with torch.no_grad():
|
|
for source_param, target_param in zip(
|
|
source.parameters(), target.parameters()
|
|
):
|
|
target_param.data.mul_(1.0 - tau)
|
|
torch.add(
|
|
target_param.data,
|
|
source_param.data,
|
|
alpha=tau,
|
|
out=target_param.data,
|
|
)
|