import torch
import torch.nn as nn
from torch.optim import Adam
from torch.autograd import Variable
import torch.nn.functional as F


class Policy(nn.Module):

    def __init__(self, hiddenSize, numInputs, numOutputs, device):
        super(Policy, self).__init__()
        self.device = device
        self.numOutputs = numOutputs
        self.numInputs = numInputs
        self.V = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, 2 * hiddenSize), nn.PReLU(2 * hiddenSize), nn.Linear
            (2 * hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, 1))
        self.mu = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, 2 * hiddenSize), nn.PReLU(2 * hiddenSize), nn.Linear
            (2 * hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, numOutputs), nn.Tanh())
        self.L = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, numOutputs * numOutputs), nn.PReLU(numOutputs *
            numOutputs), nn.Linear(numOutputs * numOutputs, numOutputs *
            numOutputs))
        self.trilMask = torch.tril(torch.ones(numOutputs, numOutputs),
            diagonal=-1).unsqueeze(0).to(device)
        self.diagMask = torch.diag(torch.ones(numOutputs, numOutputs)
            ).unsqueeze(0).to(device)

    def forward(self, inputs):
        x, u = inputs
        V = self.V(x)
        mu = self.mu(x)
        Q = None
        if u is not None:
            L = self.L(x).view(-1, self.numOutputs, self.numOutputs).to(self
                .device)
            L = L * self.trilMask.expand_as(L) + torch.exp(L
                ) * self.diagMask.expand_as(L)
            P = torch.bmm(L, L.transpose(2, 1))
            uMu = (u - mu).unsqueeze(2)
            A = -0.5 * torch.bmm(torch.bmm(uMu.transpose(2, 1), P), uMu)[:,
                :, 0]
            Q = A + V
        return mu, Q, V

3bc9349c41aa5467ffd4dca68dc71e122ec83eff
<mask token>


class Policy(nn.Module):
    <mask token>
    <mask token>

<mask token>


class Policy(nn.Module):

    def __init__(self, hiddenSize, numInputs, numOutputs, device):
        super(Policy, self).__init__()
        self.device = device
        self.numOutputs = numOutputs
        self.numInputs = numInputs
        self.V = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, 2 * hiddenSize), nn.PReLU(2 * hiddenSize), nn.Linear
            (2 * hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, 1))
        self.mu = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, 2 * hiddenSize), nn.PReLU(2 * hiddenSize), nn.Linear
            (2 * hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, numOutputs), nn.Tanh())
        self.L = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, numOutputs * numOutputs), nn.PReLU(numOutputs *
            numOutputs), nn.Linear(numOutputs * numOutputs, numOutputs *
            numOutputs))
        self.trilMask = torch.tril(torch.ones(numOutputs, numOutputs),
            diagonal=-1).unsqueeze(0).to(device)
        self.diagMask = torch.diag(torch.ones(numOutputs, numOutputs)
            ).unsqueeze(0).to(device)
    <mask token>

<mask token>


class Policy(nn.Module):

    def __init__(self, hiddenSize, numInputs, numOutputs, device):
        super(Policy, self).__init__()
        self.device = device
        self.numOutputs = numOutputs
        self.numInputs = numInputs
        self.V = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, 2 * hiddenSize), nn.PReLU(2 * hiddenSize), nn.Linear
            (2 * hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, 1))
        self.mu = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, 2 * hiddenSize), nn.PReLU(2 * hiddenSize), nn.Linear
            (2 * hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, numOutputs), nn.Tanh())
        self.L = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, numOutputs * numOutputs), nn.PReLU(numOutputs *
            numOutputs), nn.Linear(numOutputs * numOutputs, numOutputs *
            numOutputs))
        self.trilMask = torch.tril(torch.ones(numOutputs, numOutputs),
            diagonal=-1).unsqueeze(0).to(device)
        self.diagMask = torch.diag(torch.ones(numOutputs, numOutputs)
            ).unsqueeze(0).to(device)

    def forward(self, inputs):
        x, u = inputs
        V = self.V(x)
        mu = self.mu(x)
        Q = None
        if u is not None:
            L = self.L(x).view(-1, self.numOutputs, self.numOutputs).to(self
                .device)
            L = L * self.trilMask.expand_as(L) + torch.exp(L
                ) * self.diagMask.expand_as(L)
            P = torch.bmm(L, L.transpose(2, 1))
            uMu = (u - mu).unsqueeze(2)
            A = -0.5 * torch.bmm(torch.bmm(uMu.transpose(2, 1), P), uMu)[:,
                :, 0]
            Q = A + V
        return mu, Q, V

import torch
import torch.nn as nn
from torch.optim import Adam
from torch.autograd import Variable
import torch.nn.functional as F


class Policy(nn.Module):

    def __init__(self, hiddenSize, numInputs, numOutputs, device):
        super(Policy, self).__init__()
        self.device = device
        self.numOutputs = numOutputs
        self.numInputs = numInputs
        self.V = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, 2 * hiddenSize), nn.PReLU(2 * hiddenSize), nn.Linear
            (2 * hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, 1))
        self.mu = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, 2 * hiddenSize), nn.PReLU(2 * hiddenSize), nn.Linear
            (2 * hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, numOutputs), nn.Tanh())
        self.L = nn.Sequential(nn.BatchNorm1d(numInputs), nn.Linear(
            numInputs, hiddenSize), nn.PReLU(hiddenSize), nn.Linear(
            hiddenSize, numOutputs * numOutputs), nn.PReLU(numOutputs *
            numOutputs), nn.Linear(numOutputs * numOutputs, numOutputs *
            numOutputs))
        self.trilMask = torch.tril(torch.ones(numOutputs, numOutputs),
            diagonal=-1).unsqueeze(0).to(device)
        self.diagMask = torch.diag(torch.ones(numOutputs, numOutputs)
            ).unsqueeze(0).to(device)

    def forward(self, inputs):
        x, u = inputs
        V = self.V(x)
        mu = self.mu(x)
        Q = None
        if u is not None:
            L = self.L(x).view(-1, self.numOutputs, self.numOutputs).to(self
                .device)
            L = L * self.trilMask.expand_as(L) + torch.exp(L
                ) * self.diagMask.expand_as(L)
            P = torch.bmm(L, L.transpose(2, 1))
            uMu = (u - mu).unsqueeze(2)
            A = -0.5 * torch.bmm(torch.bmm(uMu.transpose(2, 1), P), uMu)[:,
                :, 0]
            Q = A + V
        return mu, Q, V
