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 typing import Tuple, Optional, Union
from mlagents.trainers.torch.layers import linear_layer, Initialization, Swish
from mlagents.torch_utils import torch, nn
class Normalizer(nn.Module):
def __init__(self, vec_obs_size: int):
super().__init__()
self.register_buffer("normalization_steps", torch.tensor(1))
self.register_buffer("running_mean", torch.zeros(vec_obs_size))
self.register_buffer("running_variance", torch.ones(vec_obs_size))
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
normalized_state = torch.clamp(
(inputs - self.running_mean)
/ torch.sqrt(self.running_variance / self.normalization_steps),
-5,
5,
)
return normalized_state
def update(self, vector_input: torch.Tensor) -> None:
steps_increment = vector_input.size()[0]
total_new_steps = self.normalization_steps + steps_increment
input_to_old_mean = vector_input - self.running_mean
new_mean = self.running_mean + (input_to_old_mean / total_new_steps).sum(0)
input_to_new_mean = vector_input - new_mean
new_variance = self.running_variance + (
input_to_new_mean * input_to_old_mean
).sum(0)
# Update in-place
self.running_mean.data.copy_(new_mean.data)
self.running_variance.data.copy_(new_variance.data)
self.normalization_steps.data.copy_(total_new_steps.data)
def copy_from(self, other_normalizer: "Normalizer") -> None:
self.normalization_steps.data.copy_(other_normalizer.normalization_steps.data)
self.running_mean.data.copy_(other_normalizer.running_mean.data)
self.running_variance.copy_(other_normalizer.running_variance.data)
def conv_output_shape(
h_w: Tuple[int, int],
kernel_size: Union[int, Tuple[int, int]] = 1,
stride: int = 1,
padding: int = 0,
dilation: int = 1,
) -> Tuple[int, int]:
"""
Calculates the output shape (height and width) of the output of a convolution layer.
kernel_size, stride, padding and dilation correspond to the inputs of the
torch.nn.Conv2d layer (https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html)
:param h_w: The height and width of the input.
:param kernel_size: The size of the kernel of the convolution (can be an int or a
tuple [width, height])
:param stride: The stride of the convolution
:param padding: The padding of the convolution
:param dilation: The dilation of the convolution
"""
from math import floor
if not isinstance(kernel_size, tuple):
kernel_size = (int(kernel_size), int(kernel_size))
h = floor(
((h_w[0] + (2 * padding) - (dilation * (kernel_size[0] - 1)) - 1) / stride) + 1
)
w = floor(
((h_w[1] + (2 * padding) - (dilation * (kernel_size[1] - 1)) - 1) / stride) + 1
)
return h, w
def pool_out_shape(h_w: Tuple[int, int], kernel_size: int) -> Tuple[int, int]:
"""
Calculates the output shape (height and width) of the output of a max pooling layer.
kernel_size corresponds to the inputs of the
torch.nn.MaxPool2d layer (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html)
:param kernel_size: The size of the kernel of the convolution
"""
height = (h_w[0] - kernel_size) // 2 + 1
width = (h_w[1] - kernel_size) // 2 + 1
return height, width
class VectorInput(nn.Module):
def __init__(self, input_size: int, normalize: bool = False):
super().__init__()
self.normalizer: Optional[Normalizer] = None
if normalize:
self.normalizer = Normalizer(input_size)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
if self.normalizer is not None:
inputs = self.normalizer(inputs)
return inputs
def copy_normalization(self, other_input: "VectorInput") -> None:
if self.normalizer is not None and other_input.normalizer is not None:
self.normalizer.copy_from(other_input.normalizer)
def update_normalization(self, inputs: torch.Tensor) -> None:
if self.normalizer is not None:
self.normalizer.update(inputs)
class SmallVisualEncoder(nn.Module):
"""
CNN architecture used by King in their Candy Crush predictor
https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning
"""
def __init__(
self, height: int, width: int, initial_channels: int, output_size: int
):
super().__init__()
self.h_size = output_size
conv_1_hw = conv_output_shape((height, width), 3, 1)
conv_2_hw = conv_output_shape(conv_1_hw, 3, 1)
self.final_flat = conv_2_hw[0] * conv_2_hw[1] * 144
self.conv_layers = nn.Sequential(
nn.Conv2d(initial_channels, 35, [3, 3], [1, 1]),
nn.LeakyReLU(),
nn.Conv2d(35, 144, [3, 3], [1, 1]),
nn.LeakyReLU(),
)
self.dense = nn.Sequential(
linear_layer(
self.final_flat,
self.h_size,
kernel_init=Initialization.KaimingHeNormal,
kernel_gain=1.0,
),
nn.LeakyReLU(),
)
def forward(self, visual_obs: torch.Tensor) -> torch.Tensor:
hidden = self.conv_layers(visual_obs)
hidden = torch.reshape(hidden, (-1, self.final_flat))
return self.dense(hidden)
class SimpleVisualEncoder(nn.Module):
def __init__(
self, height: int, width: int, initial_channels: int, output_size: int
):
super().__init__()
self.h_size = output_size
conv_1_hw = conv_output_shape((height, width), 8, 4)
conv_2_hw = conv_output_shape(conv_1_hw, 4, 2)
self.final_flat = conv_2_hw[0] * conv_2_hw[1] * 32
self.conv_layers = nn.Sequential(
nn.Conv2d(initial_channels, 16, [8, 8], [4, 4]),
nn.LeakyReLU(),
nn.Conv2d(16, 32, [4, 4], [2, 2]),
nn.LeakyReLU(),
)
self.dense = nn.Sequential(
linear_layer(
self.final_flat,
self.h_size,
kernel_init=Initialization.KaimingHeNormal,
kernel_gain=1.0,
),
nn.LeakyReLU(),
)
def forward(self, visual_obs: torch.Tensor) -> torch.Tensor:
hidden = self.conv_layers(visual_obs)
hidden = torch.reshape(hidden, (-1, self.final_flat))
return self.dense(hidden)
class NatureVisualEncoder(nn.Module):
def __init__(
self, height: int, width: int, initial_channels: int, output_size: int
):
super().__init__()
self.h_size = output_size
conv_1_hw = conv_output_shape((height, width), 8, 4)
conv_2_hw = conv_output_shape(conv_1_hw, 4, 2)
conv_3_hw = conv_output_shape(conv_2_hw, 3, 1)
self.final_flat = conv_3_hw[0] * conv_3_hw[1] * 64
self.conv_layers = nn.Sequential(
nn.Conv2d(initial_channels, 32, [8, 8], [4, 4]),
nn.LeakyReLU(),
nn.Conv2d(32, 64, [4, 4], [2, 2]),
nn.LeakyReLU(),
nn.Conv2d(64, 64, [3, 3], [1, 1]),
nn.LeakyReLU(),
)
self.dense = nn.Sequential(
linear_layer(
self.final_flat,
self.h_size,
kernel_init=Initialization.KaimingHeNormal,
kernel_gain=1.0,
),
nn.LeakyReLU(),
)
def forward(self, visual_obs: torch.Tensor) -> torch.Tensor:
hidden = self.conv_layers(visual_obs)
hidden = hidden.view([-1, self.final_flat])
return self.dense(hidden)
class ResNetBlock(nn.Module):
def __init__(self, channel: int):
"""
Creates a ResNet Block.
:param channel: The number of channels in the input (and output) tensors of the
convolutions
"""
super().__init__()
self.layers = nn.Sequential(
Swish(),
nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1),
Swish(),
nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1),
)
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
return input_tensor + self.layers(input_tensor)
class ResNetVisualEncoder(nn.Module):
def __init__(
self, height: int, width: int, initial_channels: int, output_size: int
):
super().__init__()
n_channels = [16, 32, 32] # channel for each stack
n_blocks = 2 # number of residual blocks
layers = []
last_channel = initial_channels
for _, channel in enumerate(n_channels):
layers.append(nn.Conv2d(last_channel, channel, [3, 3], [1, 1], padding=1))
layers.append(nn.MaxPool2d([3, 3], [2, 2]))
height, width = pool_out_shape((height, width), 3)
for _ in range(n_blocks):
layers.append(ResNetBlock(channel))
last_channel = channel
layers.append(Swish())
self.dense = linear_layer(
n_channels[-1] * height * width,
output_size,
kernel_init=Initialization.KaimingHeNormal,
kernel_gain=1.0,
)
self.sequential = nn.Sequential(*layers)
def forward(self, visual_obs: torch.Tensor) -> torch.Tensor:
batch_size = visual_obs.shape[0]
hidden = self.sequential(visual_obs)
before_out = hidden.view(batch_size, -1)
return torch.relu(self.dense(before_out))