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model_crawler.py
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model_crawler.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1. / np.sqrt(fan_in)
return (-lim, lim)
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units, fc2_units):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
seed (int): Random seed
fc1_units (int): Number of nodes in first hidden layer
fc2_units (int): Number of nodes in second hidden layer
"""
super(Actor, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1_bn = nn.BatchNorm1d(state_size)
self.fc1 = nn.Linear(state_size, fc1_units)
self.fc2_bn = nn.BatchNorm1d(fc1_units)
self.fc2 = nn.Linear(fc1_units, fc2_units)
self.fc2a_bn = nn.BatchNorm1d(fc2_units)
self.fc2a = nn.Linear(fc2_units, fc2_units)
self.fc3_bn = nn.BatchNorm1d(fc2_units)
self.fc3 = nn.Linear(fc2_units, action_size)
self.reset_parameters()
def reset_parameters(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc2a.weight.data.uniform_(*hidden_init(self.fc2a))
self.fc3.weight.data.uniform_(-3e-3, 3e-3)
def forward(self, state):
"""Build an actor (policy) network that maps states -> actions."""
x = F.relu(self.fc1(self.fc1_bn(state)))
x = F.relu(self.fc2(self.fc2_bn(x)))
x = F.relu(self.fc2a(self.fc2a_bn(x)))
return F.tanh(self.fc3(self.fc3_bn(x)))
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, seed, fc1_units, fc2_units):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
seed (int): Random seed
fcs1_units (int): Number of nodes in the first hidden layer
fc2_units (int): Number of nodes in the second hidden layer
"""
super(Critic, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1_bn = nn.BatchNorm1d(state_size)
self.fc1 = nn.Linear(state_size, fc1_units)
self.fc2_bn = nn.BatchNorm1d(fc1_units)
self.fc2 = nn.Linear(fc1_units, fc2_units)
self.fc2a_bn = nn.BatchNorm1d(fc2_units)
self.fc2a = nn.Linear(fc2_units, fc2_units)
self.fc3_bn = nn.BatchNorm1d(fc2_units)
self.fc3 = nn.Linear(fc2_units, 1)
self.reset_parameters()
def reset_parameters(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc2a.weight.data.uniform_(*hidden_init(self.fc2a))
self.fc3.weight.data.uniform_(-3e-3, 3e-3)
def forward(self, state):
"""Build an critic (value) network that maps states -> value."""
x = F.relu(self.fc1(self.fc1_bn(state)))
x = F.relu(self.fc2(self.fc2_bn(x)))
x = F.relu(self.fc2a(self.fc2a_bn(x)))
return F.tanh(self.fc3(self.fc3_bn(x)))
class PPO_Actor_Critic(nn.Module):
# def __init__(self, state_size, action_size, seed, fc1_units=1024, fc2_units=1024):
def __init__(self, state_size, action_size, seed, fc1_units=128, fc2_units=128):
super(PPO_Actor_Critic, self).__init__()
self.actor = Actor(state_size, action_size, seed, fc1_units, fc2_units)
self.critic = Critic(state_size, seed, fc1_units, fc2_units)
self.std = nn.Parameter(torch.ones(1, action_size)*0.15)
def forward(self, state, action=None, scale=1.):
"""Build Policy.
Returns
======
action (Tensor): predicted action or inputed action
log_prob (Tensor): log probability of current action distribution
ent (Tensor): entropy of current action distribution
value (Tensor): estimate value function
"""
action_mean = self.actor(state)
value = self.critic(state)
dist = torch.distributions.Normal(action_mean, F.hardtanh(self.std, min_val=0.06*scale, max_val=0.6*scale))
if action is None:
action = dist.sample()
log_prob = dist.log_prob(action)
log_prob = torch.sum(log_prob, dim=1, keepdim=True)
ent = dist.entropy().mean()
return action, log_prob, ent, value