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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import abc
from typing import List
from mlagents.torch_utils import torch, nn
import numpy as np
import math
from mlagents.trainers.torch.layers import linear_layer, Initialization
EPSILON = 1e-7 # Small value to avoid divide by zero
class DistInstance(nn.Module, abc.ABC):
@abc.abstractmethod
def sample(self) -> torch.Tensor:
"""
Return a sample from this distribution.
"""
pass
@abc.abstractmethod
def log_prob(self, value: torch.Tensor) -> torch.Tensor:
"""
Returns the log probabilities of a particular value.
:param value: A value sampled from the distribution.
:returns: Log probabilities of the given value.
"""
pass
@abc.abstractmethod
def entropy(self) -> torch.Tensor:
"""
Returns the entropy of this distribution.
"""
pass
@abc.abstractmethod
def exported_model_output(self) -> torch.Tensor:
"""
Returns the tensor to be exported to ONNX for the distribution
"""
pass
class DiscreteDistInstance(DistInstance):
@abc.abstractmethod
def all_log_prob(self) -> torch.Tensor:
"""
Returns the log probabilities of all actions represented by this distribution.
"""
pass
class GaussianDistInstance(DistInstance):
def __init__(self, mean, std):
super().__init__()
self.mean = mean
self.std = std
def sample(self):
sample = self.mean + torch.randn_like(self.mean) * self.std
return sample
def log_prob(self, value):
var = self.std ** 2
log_scale = torch.log(self.std + EPSILON)
return (
-((value - self.mean) ** 2) / (2 * var + EPSILON)
- log_scale
- math.log(math.sqrt(2 * math.pi))
)
def pdf(self, value):
log_prob = self.log_prob(value)
return torch.exp(log_prob)
def entropy(self):
return torch.mean(
0.5 * torch.log(2 * math.pi * math.e * self.std + EPSILON),
dim=1,
keepdim=True,
) # Use equivalent behavior to TF
def exported_model_output(self):
return self.sample()
class TanhGaussianDistInstance(GaussianDistInstance):
def __init__(self, mean, std):
super().__init__(mean, std)
self.transform = torch.distributions.transforms.TanhTransform(cache_size=1)
def sample(self):
unsquashed_sample = super().sample()
squashed = self.transform(unsquashed_sample)
return squashed
def _inverse_tanh(self, value):
capped_value = torch.clamp(value, -1 + EPSILON, 1 - EPSILON)
return 0.5 * torch.log((1 + capped_value) / (1 - capped_value) + EPSILON)
def log_prob(self, value):
unsquashed = self.transform.inv(value)
return super().log_prob(unsquashed) - self.transform.log_abs_det_jacobian(
unsquashed, value
)
class CategoricalDistInstance(DiscreteDistInstance):
def __init__(self, logits):
super().__init__()
self.logits = logits
self.probs = torch.softmax(self.logits, dim=-1)
def sample(self):
return torch.multinomial(self.probs, 1)
def pdf(self, value):
# This function is equivalent to torch.diag(self.probs.T[value.flatten().long()]),
# but torch.diag is not supported by ONNX export.
idx = torch.arange(start=0, end=len(value)).unsqueeze(-1)
return torch.gather(
self.probs.permute(1, 0)[value.flatten().long()], -1, idx
).squeeze(-1)
def log_prob(self, value):
return torch.log(self.pdf(value) + EPSILON)
def all_log_prob(self):
return torch.log(self.probs + EPSILON)
def entropy(self):
return -torch.sum(
self.probs * torch.log(self.probs + EPSILON), dim=-1
).unsqueeze(-1)
def exported_model_output(self):
return self.all_log_prob()
class GaussianDistribution(nn.Module):
def __init__(
self,
hidden_size: int,
num_outputs: int,
conditional_sigma: bool = False,
tanh_squash: bool = False,
):
super().__init__()
self.conditional_sigma = conditional_sigma
self.mu = linear_layer(
hidden_size,
num_outputs,
kernel_init=Initialization.KaimingHeNormal,
kernel_gain=0.2,
bias_init=Initialization.Zero,
)
self.tanh_squash = tanh_squash
if conditional_sigma:
self.log_sigma = linear_layer(
hidden_size,
num_outputs,
kernel_init=Initialization.KaimingHeNormal,
kernel_gain=0.2,
bias_init=Initialization.Zero,
)
else:
self.log_sigma = nn.Parameter(
torch.zeros(1, num_outputs, requires_grad=True)
)
def forward(self, inputs: torch.Tensor) -> List[DistInstance]:
mu = self.mu(inputs)
if self.conditional_sigma:
log_sigma = torch.clamp(self.log_sigma(inputs), min=-20, max=2)
else:
# Expand so that entropy matches batch size. Note that we're using
# mu*0 here to get the batch size implicitly since Barracuda 1.2.1
# throws error on runtime broadcasting due to unknown reason. We
# use this to replace torch.expand() becuase it is not supported in
# the verified version of Barracuda (1.0.X).
log_sigma = mu * 0 + self.log_sigma
if self.tanh_squash:
return TanhGaussianDistInstance(mu, torch.exp(log_sigma))
else:
return GaussianDistInstance(mu, torch.exp(log_sigma))
class MultiCategoricalDistribution(nn.Module):
def __init__(self, hidden_size: int, act_sizes: List[int]):
super().__init__()
self.act_sizes = act_sizes
self.branches = self._create_policy_branches(hidden_size)
def _create_policy_branches(self, hidden_size: int) -> nn.ModuleList:
branches = []
for size in self.act_sizes:
branch_output_layer = linear_layer(
hidden_size,
size,
kernel_init=Initialization.KaimingHeNormal,
kernel_gain=0.1,
bias_init=Initialization.Zero,
)
branches.append(branch_output_layer)
return nn.ModuleList(branches)
def _mask_branch(
self, logits: torch.Tensor, allow_mask: torch.Tensor
) -> torch.Tensor:
# Zero out masked logits, then subtract a large value. Technique mentionend here:
# https://arxiv.org/abs/2006.14171. Our implementation is ONNX and Barracuda-friendly.
block_mask = -1.0 * allow_mask + 1.0
# We do -1 * tensor + constant instead of constant - tensor because it seems
# Barracuda might swap the inputs of a "Sub" operation
logits = logits * allow_mask - 1e8 * block_mask
return logits
def _split_masks(self, masks: torch.Tensor) -> List[torch.Tensor]:
split_masks = []
for idx, _ in enumerate(self.act_sizes):
start = int(np.sum(self.act_sizes[:idx]))
end = int(np.sum(self.act_sizes[: idx + 1]))
split_masks.append(masks[:, start:end])
return split_masks
def forward(self, inputs: torch.Tensor, masks: torch.Tensor) -> List[DistInstance]:
# Todo - Support multiple branches in mask code
branch_distributions = []
masks = self._split_masks(masks)
for idx, branch in enumerate(self.branches):
logits = branch(inputs)
norm_logits = self._mask_branch(logits, masks[idx])
distribution = CategoricalDistInstance(norm_logits)
branch_distributions.append(distribution)
return branch_distributions