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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from distributions import Categorical, DiagGaussian
from utils import orthogonal
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1 or classname.find('Linear') != -1:
orthogonal(m.weight.data)
if m.bias is not None:
m.bias.data.fill_(0)
class FFPolicy(nn.Module):
def __init__(self):
super(FFPolicy, self).__init__()
def forward(self, inputs, states, masks):
raise NotImplementedError
def act(self, inputs, states, masks, deterministic=False):
value, x, states = self(inputs, states, masks)
action = self.dist.sample(x, deterministic=deterministic)
action_log_probs, dist_entropy = self.dist.logprobs_and_entropy(x, action)
return value, action, action_log_probs, states
def evaluate_actions(self, inputs, states, masks, actions):
value, x, states = self(inputs, states, masks)
action_log_probs, dist_entropy = self.dist.logprobs_and_entropy(x, actions)
return value, action_log_probs, dist_entropy, states
class CNNPolicy(FFPolicy):
def __init__(self, num_inputs, action_space, use_gru):
super(CNNPolicy, self).__init__()
self.conv1 = nn.Conv2d(num_inputs, 32, 8, stride=4)
self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
self.conv3 = nn.Conv2d(64, 32, 3, stride=1)
self.linear1 = nn.Linear(32 * 7 * 7, 512)
if use_gru:
self.gru = nn.GRUCell(512, 512)
self.critic_linear = nn.Linear(512, 1)
if action_space.__class__.__name__ == "Discrete":
num_outputs = action_space.n
self.dist = Categorical(512, num_outputs)
elif action_space.__class__.__name__ == "Box":
num_outputs = action_space.shape[0]
self.dist = DiagGaussian(512, num_outputs)
else:
raise NotImplementedError
self.train()
self.reset_parameters()
@property
def state_size(self):
if hasattr(self, 'gru'):
return 512
else:
return 1
def reset_parameters(self):
self.apply(weights_init)
relu_gain = nn.init.calculate_gain('relu')
self.conv1.weight.data.mul_(relu_gain)
self.conv2.weight.data.mul_(relu_gain)
self.conv3.weight.data.mul_(relu_gain)
self.linear1.weight.data.mul_(relu_gain)
if hasattr(self, 'gru'):
orthogonal(self.gru.weight_ih.data)
orthogonal(self.gru.weight_hh.data)
self.gru.bias_ih.data.fill_(0)
self.gru.bias_hh.data.fill_(0)
if self.dist.__class__.__name__ == "DiagGaussian":
self.dist.fc_mean.weight.data.mul_(0.01)
def forward(self, inputs, states, masks):
x = self.conv1(inputs / 255.0)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = self.conv3(x)
x = F.relu(x)
x = x.view(-1, 32 * 7 * 7)
x = self.linear1(x)
x = F.relu(x)
if hasattr(self, 'gru'):
if inputs.size(0) == states.size(0):
x = states = self.gru(x, states * masks)
else:
x = x.view(-1, states.size(0), x.size(1))
masks = masks.view(-1, states.size(0), 1)
outputs = []
for i in range(x.size(0)):
hx = states = self.gru(x[i], states * masks[i])
outputs.append(hx)
x = torch.cat(outputs, 0)
return self.critic_linear(x), x, states
def weights_init_mlp(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0, 1)
m.weight.data *= 1 / torch.sqrt(m.weight.data.pow(2).sum(1, keepdim=True))
if m.bias is not None:
m.bias.data.fill_(0)
class MLPPolicy(FFPolicy):
def __init__(self, num_inputs, action_space):
super(MLPPolicy, self).__init__()
self.action_space = action_space
self.a_fc1 = nn.Linear(num_inputs, 64)
self.a_fc2 = nn.Linear(64, 64)
self.v_fc1 = nn.Linear(num_inputs, 64)
self.v_fc2 = nn.Linear(64, 64)
self.v_fc3 = nn.Linear(64, 1)
if action_space.__class__.__name__ == "Discrete":
num_outputs = action_space.n
self.dist = Categorical(64, num_outputs)
elif action_space.__class__.__name__ == "Box":
num_outputs = action_space.shape[0]
self.dist = DiagGaussian(64, num_outputs)
else:
raise NotImplementedError
self.train()
self.reset_parameters()
@property
def state_size(self):
return 1
def reset_parameters(self):
self.apply(weights_init_mlp)
"""
tanh_gain = nn.init.calculate_gain('tanh')
self.a_fc1.weight.data.mul_(tanh_gain)
self.a_fc2.weight.data.mul_(tanh_gain)
self.v_fc1.weight.data.mul_(tanh_gain)
self.v_fc2.weight.data.mul_(tanh_gain)
"""
if self.dist.__class__.__name__ == "DiagGaussian":
self.dist.fc_mean.weight.data.mul_(0.01)
def forward(self, inputs, states, masks):
x = self.v_fc1(inputs)
x = F.tanh(x)
x = self.v_fc2(x)
x = F.tanh(x)
x = self.v_fc3(x)
value = x
x = self.a_fc1(inputs)
x = F.tanh(x)
x = self.a_fc2(x)
x = F.tanh(x)
return value, x, states