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models.py
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models.py
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import os
import torch as T
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from torch.nn import init, Parameter
import math
from torch.autograd import Variable
###############################################
## Naive Q Network
###############################################
class LinearDeepQNetwork(nn.Module):
def __init__(self, lr, n_actions, input_dims):
super(LinearDeepQNetwork, self).__init__()
self.fc1 = nn.Linear(*input_dims, 128)
self.fc2 = nn.Linear(128, n_actions)
self.optimizer = optim.Adam(self.parameters(), lr=lr)
self.loss = nn.MSELoss()
self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
self.to(self.device)
def forward(self, state):
layer1 = F.relu(self.fc1(state))
actions = self.fc2(layer1)
return actions
###############################################
## Deep Q Network
###############################################
class DeepQNetwork(nn.Module):
def __init__(self, lr, n_actions, name, input_dims, chkpt_dir):
super(DeepQNetwork, self).__init__()
self.checkpoint_dir = chkpt_dir
self.checkpoint_file = os.path.join(self.checkpoint_dir, name)
self.conv1 = nn.Conv2d(input_dims[0], 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = nn.Conv2d(64, 64, 3, stride=1)
fc_input_dims = self.calculate_conv_output_dims(input_dims)
self.fc1 = nn.Linear(fc_input_dims, 512)
self.fc2 = nn.Linear(512, n_actions)
self.optimizer = optim.RMSprop(self.parameters(), lr=lr)
self.loss = nn.MSELoss()
self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
self.to(self.device)
def calculate_conv_output_dims(self, input_dims):
state = T.zeros(1, *input_dims)
dims = self.conv1(state)
dims = self.conv2(dims)
dims = self.conv3(dims)
return int(np.prod(dims.size()))
def forward(self, state):
conv1 = F.relu(self.conv1(state))
conv2 = F.relu(self.conv2(conv1))
conv3 = F.relu(self.conv3(conv2))
# conv3 shape is BS x n_filters x H x W
conv_state = conv3.view(conv3.size()[0], -1)
# conv_state shape is BS x (n_filters * H * W)
flat1 = F.relu(self.fc1(conv_state))
actions = self.fc2(flat1)
return actions
def save_checkpoint(self):
print('... saving checkpoint ...')
T.save(self.state_dict(), self.checkpoint_file)
def load_checkpoint(self):
print('... loading checkpoint ...')
self.load_state_dict(T.load(self.checkpoint_file))
###############################################
## Dueling Deep Q Network
###############################################
class DuelingDeepQNetwork(nn.Module):
def __init__(self, lr, n_actions, name, input_dims, chkpt_dir):
super(DuelingDeepQNetwork, self).__init__()
self.checkpoint_dir = chkpt_dir
self.checkpoint_file = os.path.join(self.checkpoint_dir, name)
self.conv1 = nn.Conv2d(input_dims[0], 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = nn.Conv2d(64, 64, 3, stride=1)
fc_input_dims = self.calculate_conv_output_dims(input_dims)
self.fc1 = nn.Linear(fc_input_dims, 1024)
self.fc2 = nn.Linear(1024, 512)
self.V = nn.Linear(512, 1)
self.A = nn.Linear(512, n_actions)
self.optimizer = optim.RMSprop(self.parameters(), lr=lr)
self.loss = nn.MSELoss()
self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
self.to(self.device)
def calculate_conv_output_dims(self, input_dims):
state = T.zeros(1, *input_dims)
dims = self.conv1(state)
dims = self.conv2(dims)
dims = self.conv3(dims)
return int(np.prod(dims.size()))
def forward(self, state):
conv1 = F.relu(self.conv1(state))
conv2 = F.relu(self.conv2(conv1))
conv3 = F.relu(self.conv3(conv2))
conv_state = conv3.view(conv3.size()[0], -1)
flat1 = F.relu(self.fc1(conv_state))
flat2 = F.relu(self.fc2(flat1))
V = self.V(flat2)
A = self.A(flat2)
return V, A
def save_checkpoint(self):
print('... saving checkpoint ...')
T.save(self.state_dict(), self.checkpoint_file)
def load_checkpoint(self):
print('... loading checkpoint ...')
self.load_state_dict(T.load(self.checkpoint_file))
# Noisy linear layer with independent Gaussian noise
# class NoisyLinear(nn.Linear):
# def __init__(self, in_features, out_features, sigma_init=0.017, bias=True):
# super(NoisyLinear, self).__init__(in_features, out_features, bias=True) # TODO: Adapt for no bias
# # µ^w and µ^b reuse self.weight and self.bias
# self.sigma_init = sigma_init
# self.sigma_weight = Parameter(T.Tensor(out_features, in_features)) # σ^w
# self.sigma_bias = Parameter(T.Tensor(out_features)) # σ^b
# self.register_buffer('epsilon_weight', T.zeros(out_features, in_features))
# self.register_buffer('epsilon_bias', T.zeros(out_features))
# self.reset_parameters()
# def reset_parameters(self):
# if hasattr(self, 'sigma_weight'): # Only init after all params added (otherwise super().__init__() fails)
# init.uniform(self.weight, -math.sqrt(3 / self.in_features), math.sqrt(3 / self.in_features))
# init.uniform(self.bias, -math.sqrt(3 / self.in_features), math.sqrt(3 / self.in_features))
# init.constant(self.sigma_weight, self.sigma_init)
# init.constant(self.sigma_bias, self.sigma_init)
# def forward(self, input):
# return F.linear(input, self.weight + self.sigma_weight * Variable(self.epsilon_weight), self.bias + self.sigma_bias * Variable(self.epsilon_bias))
# def sample_noise(self):
# self.epsilon_weight = T.randn(self.out_features, self.in_features)
# self.epsilon_bias = T.randn(self.out_features)
# def remove_noise(self):
# self.epsilon_weight = T.zeros(self.out_features, self.in_features)
# self.epsilon_bias = T.zeros(self.out_features)
# class ActorCritic(nn.Module):
# def __init__(self, observation_space, action_space, hidden_size, sigma_init, no_noise):
# super(ActorCritic, self).__init__()
# self.no_noise = no_noise
# self.state_size = observation_space.shape[0]
# self.action_size = action_space.n
# self.relu = nn.ReLU(inplace=True)
# self.softmax = nn.Softmax(dim=1)
# self.fc1 = nn.Linear(self.state_size, hidden_size)
# self.lstm = nn.LSTMCell(hidden_size, hidden_size)
# if no_noise:
# self.fc_actor = nn.Linear(hidden_size, self.action_size)
# self.fc_critic = nn.Linear(hidden_size, 1)
# else:
# self.fc_actor = NoisyLinear(hidden_size, self.action_size, sigma_init=sigma_init)
# self.fc_critic = NoisyLinear(hidden_size, 1, sigma_init=sigma_init)
# def forward(self, x, h):
# x = self.relu(self.fc1(x))
# h = self.lstm(x, h) # h is (hidden state, cell state)
# x = h[0]
# policy = self.softmax(self.fc_actor(x)).clamp(max=1 - 1e-20) # Prevent 1s and hence NaNs
# V = self.fc_critic(x)
# return policy, V, (h[0], h[1])
# def sample_noise(self):
# if not self.no_noise:
# self.fc_actor.sample_noise()
# self.fc_critic.sample_noise()
# def remove_noise(self):
# if not self.no_noise:
# self.fc_actor.remove_noise()
# self.fc_critic.remove_noise()
# def save_checkpoint(self):
# print('... saving checkpoint ...')
# T.save(self.state_dict(), self.checkpoint_file)
# def load_checkpoint(self):
# print('... loading checkpoint ...')
# self.load_state_dict(T.load(self.checkpoint_file))