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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ding.torch_utils import Swish
class StandardScaler(nn.Module):
def __init__(self, input_size: int):
super(StandardScaler, self).__init__()
self.register_buffer('std', torch.ones(1, input_size))
self.register_buffer('mu', torch.zeros(1, input_size))
def fit(self, data: torch.Tensor):
std, mu = torch.std_mean(data, dim=0, keepdim=True)
std[std < 1e-12] = 1
self.std.data.mul_(0.0).add_(std)
self.mu.data.mul_(0.0).add_(mu)
def transform(self, data: torch.Tensor):
return (data - self.mu) / self.std
def inverse_transform(self, data: torch.Tensor):
return self.std * data + self.mu
class EnsembleFC(nn.Module):
__constants__ = ['in_features', 'out_features']
in_features: int
out_features: int
ensemble_size: int
weight: torch.Tensor
def __init__(self, in_features: int, out_features: int, ensemble_size: int, weight_decay: float = 0.) -> None:
super(EnsembleFC, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.ensemble_size = ensemble_size
self.weight = nn.Parameter(torch.zeros(ensemble_size, in_features, out_features))
self.weight_decay = weight_decay
self.bias = nn.Parameter(torch.zeros(ensemble_size, 1, out_features))
def forward(self, input: torch.Tensor) -> torch.Tensor:
assert input.shape[0] == self.ensemble_size and len(input.shape) == 3
return torch.bmm(input, self.weight) + self.bias # w times x + b
def extra_repr(self) -> str:
return 'in_features={}, out_features={}, ensemble_size={}, weight_decay={}'.format(
self.in_features, self.out_features, self.ensemble_size, self.weight_decay
)
class EnsembleModel(nn.Module):
def __init__(
self,
state_size,
action_size,
reward_size,
ensemble_size,
hidden_size=200,
learning_rate=1e-3,
use_decay=False
):
super(EnsembleModel, self).__init__()
self.use_decay = use_decay
self.hidden_size = hidden_size
self.output_dim = state_size + reward_size
self.nn1 = EnsembleFC(state_size + action_size, hidden_size, ensemble_size, weight_decay=0.000025)
self.nn2 = EnsembleFC(hidden_size, hidden_size, ensemble_size, weight_decay=0.00005)
self.nn3 = EnsembleFC(hidden_size, hidden_size, ensemble_size, weight_decay=0.000075)
self.nn4 = EnsembleFC(hidden_size, hidden_size, ensemble_size, weight_decay=0.000075)
self.nn5 = EnsembleFC(hidden_size, self.output_dim * 2, ensemble_size, weight_decay=0.0001)
self.max_logvar = nn.Parameter(torch.ones(1, self.output_dim).float() * 0.5, requires_grad=False)
self.min_logvar = nn.Parameter(torch.ones(1, self.output_dim).float() * -10, requires_grad=False)
self.swish = Swish()
def init_weights(m: nn.Module):
def truncated_normal_init(t, mean: float = 0.0, std: float = 0.01):
torch.nn.init.normal_(t, mean=mean, std=std)
while True:
cond = torch.logical_or(t < mean - 2 * std, t > mean + 2 * std)
if not torch.sum(cond):
break
t = torch.where(cond, torch.nn.init.normal_(torch.ones(t.shape), mean=mean, std=std), t)
return t
if isinstance(m, nn.Linear) or isinstance(m, EnsembleFC):
input_dim = m.in_features
truncated_normal_init(m.weight, std=1 / (2 * np.sqrt(input_dim)))
m.bias.data.fill_(0.0)
self.apply(init_weights)
self.optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate)
def forward(self, x: torch.Tensor, ret_log_var: bool = False):
x = self.swish(self.nn1(x))
x = self.swish(self.nn2(x))
x = self.swish(self.nn3(x))
x = self.swish(self.nn4(x))
x = self.nn5(x)
mean, logvar = x.chunk(2, dim=2)
logvar = self.max_logvar - F.softplus(self.max_logvar - logvar)
logvar = self.min_logvar + F.softplus(logvar - self.min_logvar)
if ret_log_var:
return mean, logvar
else:
return mean, torch.exp(logvar)
def get_decay_loss(self):
decay_loss = 0.
for m in self.modules():
if isinstance(m, EnsembleFC):
decay_loss += m.weight_decay * torch.sum(torch.square(m.weight)) / 2.
return decay_loss
def loss(self, mean: torch.Tensor, logvar: torch.Tensor, labels: torch.Tensor):
"""
mean, logvar: Ensemble_size x N x dim
labels: Ensemble_size x N x dim
"""
assert len(mean.shape) == len(logvar.shape) == len(labels.shape) == 3
inv_var = torch.exp(-logvar)
# Average over batch and dim, sum over ensembles.
mse_loss_inv = (torch.pow(mean - labels, 2) * inv_var).mean(dim=(1, 2))
var_loss = logvar.mean(dim=(1, 2))
with torch.no_grad():
# Used only for logging.
mse_loss = torch.pow(mean - labels, 2).mean(dim=(1, 2))
total_loss = mse_loss_inv.sum() + var_loss.sum()
return total_loss, mse_loss
def train(self, loss: torch.Tensor):
self.optimizer.zero_grad()
loss += 0.01 * torch.sum(self.max_logvar) - 0.01 * torch.sum(self.min_logvar)
if self.use_decay:
loss += self.get_decay_loss()
loss.backward()
self.optimizer.step()
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