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model.py
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model.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, fc_units=256):
"""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 = nn.Linear(state_size, fc_units)
self.fc2 = nn.Linear(fc_units, action_size)
self.tim = 5_000_000
self.reset_parameters()
def reset_parameters(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(-3e-3, 3e-3)
def forward(self, state):
"""Build an actor (policy) network that maps states -> actions."""
#np_error1 = np.random.normal(0, 1, state.size())# * (self.tim/5_000_000)
#np_error2 = np.random.normal(0, 1, self.fc2.weight.data.size()) * (self.tim/5_000_000)
# self.tim = self.tim - 1
# np_error1 = np.random.normal(0, 1, state.size())
# param_error1 = torch.tensor(np_error1).type(torch.FloatTensor)
# state = state+param_error1
# if (5_000_000 % self.tim == 0):
# print (self.tim/5_000_000)
# # np_error1 = np_error1/np_error1.sum()
# # np_error2 = np_error2/np_error1.sum()
#param_error1 = torch.tensor(np_error1).type(torch.FloatTensor)
# param_error1 = torch.tensor(np_error1).type(torch.FloatTensor)
# param_error2 = torch.tensor(np_error2).type(torch.FloatTensor)
# #param_error = param_error.double()
# # print(self.fc1.weight.data.size())
# # print(self.fc2.weight.data.size())
# # print(param_error1)
# # print(param_error2)
# #exit()
# self.fc1.weight.data = self.fc1.weight.data + param_error1
# self.fc2.weight.data = self.fc2.weight.data + param_error2
# self.fc1.weight.data = self.fc1.weight.data/self.fc1.weight.data.sum()
# self.fc2.weight.data = self.fc2.weight.data/self.fc2.weight.data.sum()
# print(self.fc1.weight.data)
# print(self.fc2.weight.data)
# exit()
x = F.relu(self.fc1(state))
return F.tanh(self.fc2(x))
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, fcs1_units=256, fc2_units=256, fc3_units=128):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
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.fcs1 = nn.Linear(state_size, fcs1_units)
self.fc2 = nn.Linear(fcs1_units+action_size, fc2_units)
self.fc3 = nn.Linear(fc2_units, fc3_units)
self.fc4 = nn.Linear(fc3_units, 1)
self.reset_parameters()
def reset_parameters(self):
self.fcs1.weight.data.uniform_(*hidden_init(self.fcs1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(*hidden_init(self.fc3))
self.fc4.weight.data.uniform_(-3e-3, 3e-3)
def forward(self, state, action):
"""Build a critic (value) network that maps (state, action) pairs -> Q-values."""
xs = F.leaky_relu(self.fcs1(state))
x = torch.cat((xs, action), dim=1)
x = F.leaky_relu(self.fc2(x))
x = F.leaky_relu(self.fc3(x))
return self.fc4(x)