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DDPG.py
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DDPG.py
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import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import utils
import math
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3)
# Paper: https://arxiv.org/abs/1802.09477
feat_size = 1
latent_dim = 512
''' Utilities '''
class Flatten(torch.nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
class Actor(nn.Module):
def __init__(self, action_dim, img_stack):
super(Actor, self).__init__()
self.encoder = torch.nn.ModuleList([ ## input size:[96, 96]
torch.nn.Conv2d(img_stack, 16, 5, 2, padding=2), ## output size: [16, 48, 48]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(16),
torch.nn.Conv2d(16, 32, 5, 2, padding=2), ## output size: [32, 24, 24]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(32),
torch.nn.Conv2d(32, 64, 5, 2, padding=2), ## output size: [64, 12, 12]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(64),
torch.nn.Conv2d(64, 128, 5, 4, padding=2), ## output size: [128, 3, 3]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(128),
torch.nn.Conv2d(128, 256, 5, 2, padding=2), ## output size: [256, 2, 2]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(256),
torch.nn.Conv2d(256, 512, 5, 2, padding=2), ## output size: [512, 1, 1]
Flatten(), ## output: 512
])
self.linear = torch.nn.ModuleList([
torch.nn.Linear(latent_dim, 30),
torch.nn.ReLU(),
torch.nn.Linear(30, action_dim),
torch.nn.Tanh(),
])
def forward(self, x):
for layer in self.encoder:
x = layer(x)
# print(x.size())
for layer in self.linear:
x = layer(x)
# print(x.size())
return x
class Critic(nn.Module):
def __init__(self, action_dim, img_stack):
super(Critic, self).__init__()
self.encoder = torch.nn.ModuleList([ ## input size:[96, 96]
torch.nn.Conv2d(img_stack, 16, 5, 2, padding=2), ## output size: [16, 48, 48]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(16),
torch.nn.Conv2d(16, 32, 5, 2, padding=2), ## output size: [32, 24, 24]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(32),
torch.nn.Conv2d(32, 64, 5, 2, padding=2), ## output size: [64, 12, 12]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(64),
torch.nn.Conv2d(64, 128, 5, 4, padding=2), ## output size: [128, 3, 3]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(128),
torch.nn.Conv2d(128, 256, 5, 2, padding=2), ## output size: [256, 2, 2]
torch.nn.ReLU(),
torch.nn.BatchNorm2d(256),
torch.nn.Conv2d(256, 512, 5, 2, padding=2), ## output size: [512, 1, 1]
Flatten(), ## output: 512
])
self.linear = torch.nn.ModuleList([
torch.nn.Linear(latent_dim + action_dim, 30),
torch.nn.ReLU(),
torch.nn.Linear(30, 1),
])
def forward(self, x, u):
for layer in self.encoder:
x = layer(x)
counter = 0
for layer in self.linear:
counter += 1
if counter == 1:
x = torch.cat([x, u], 1)
x = layer(x)
else:
x = layer(x)
return x
class DDPG(object):
def __init__(self, action_dim, img_stack):
self.action_dim = action_dim
self.actor = Actor(action_dim, img_stack).to(device)
self.actor_target = Actor(action_dim, img_stack).to(device)
self.actor_target.load_state_dict(self.actor.state_dict())
self.actor_optimizer = torch.optim.Adam(self.actor.parameters())
self.actor_loss = []
self.critic = Critic(action_dim, img_stack).to(device)
self.critic_target = Critic(action_dim, img_stack).to(device)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic_optimizer = torch.optim.Adam(self.critic.parameters())
self.critic_loss = []
def select_action(self, state):
state = state.float().to(device)
return self.actor(state).cpu().data.numpy().flatten()
def train(self, replay_buffer, iterations, beta_PER, batch_size=100, discount=0.99, tau=0.005):
for it in range(iterations):
# print("training")
# Sample replay buffer
x, y, u, r, d, indices, w = replay_buffer.sample(batch_size, beta=beta_PER)
state = torch.FloatTensor(x).squeeze(1).to(device)
# print('state size: ' +str(state.size()))
u = u.reshape((batch_size, self.action_dim))
action = torch.FloatTensor(u).to(device)
# print('action size: ' +str(action.size()))
next_state = torch.FloatTensor(y).squeeze(1).to(device)
# print('next state size: ' +str(next_state.size()))
done = torch.FloatTensor(1 - d).to(device)
reward = torch.FloatTensor(r).to(device)
w = w.reshape((batch_size, -1))
weights = torch.FloatTensor(w).to(device)
# Compute the target Q value
target_Q = self.critic_target(next_state, self.actor_target(next_state))
target_Q = reward + (done * discount * target_Q).detach()
# Get current Q estimate
current_Q = self.critic(state, action)
# Compute critic loss
critic_loss = weights * ((current_Q - target_Q).pow(2))
prios = critic_loss + 1e-5
critic_loss = critic_loss.mean()
self.critic_loss.append(critic_loss)
# print("critic_loss"+str(critic_loss))
# Optimize the critic
self.critic_optimizer.zero_grad()
critic_loss.backward()
replay_buffer.update_priorities(indices, prios.data.cpu().numpy())
self.critic_optimizer.step()
# Compute actor loss
actor_loss = -self.critic(state, self.actor(state)).mean()
self.actor_loss.append(actor_loss)
# print("actor_loss"+ str(actor_loss))
# Optimize the actor
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# Update the frozen target models
for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
def save(self, directory, name):
torch.save(self.actor.state_dict(), '%s/%s_actor.pth' % (directory, name))
torch.save(self.actor_target.state_dict(), '%s/%s_actor_target.pth' % (directory, name))
torch.save(self.critic.state_dict(), '%s/%s_crtic_2.pth' % (directory, name))
torch.save(self.critic_target.state_dict(), '%s/%s_critic_2_target.pth' % (directory, name))
def load(self, directory, name):
self.actor.load_state_dict(
torch.load('%s/%s_actor.pth' % (directory, name), map_location=lambda storage, loc: storage))
self.actor_target.load_state_dict(
torch.load('%s/%s_actor_target.pth' % (directory, name), map_location=lambda storage, loc: storage))
self.critic.load_state_dict(
torch.load('%s/%s_crtic_2.pth' % (directory, name), map_location=lambda storage, loc: storage))
self.critic_target.load_state_dict(
torch.load('%s/%s_critic_2_target.pth' % (directory, name), map_location=lambda storage, loc: storage))
def load_actor(self, directory, name):
self.actor.load_state_dict(
torch.load('%s/%s_actor.pth' % (directory, name), map_location=lambda storage, loc: storage))
self.actor_target.load_state_dict(
torch.load('%s/%s_actor_target.pth' % (directory, name), map_location=lambda storage, loc: storage))