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ddqn_agent.py
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ddqn_agent.py
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import math
import random
from collections import deque
import airsim
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
from setuptools import glob
from env import DroneEnv
from torch.utils.tensorboard import SummaryWriter
import time
from prioritized_memory import Memory
writer = SummaryWriter()
torch.manual_seed(0)
random.seed(0)
np.random.seed(0)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
class DQN(nn.Module):
def __init__(self, in_channels=1, num_actions=4):
super(DQN, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 84, kernel_size=4, stride=4)
self.conv2 = nn.Conv2d(84, 42, kernel_size=4, stride=2)
self.conv3 = nn.Conv2d(42, 21, kernel_size=2, stride=2)
self.fc4 = nn.Linear(21 * 4 * 4, 168)
self.fc5 = nn.Linear(168, num_actions)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = x.view(x.size(0), -1)
x = F.relu(self.fc4(x))
return self.fc5(x)
class DDQN_Agent:
def __init__(self, useDepth=False):
self.useDepth = useDepth
self.eps_start = 0.9
self.eps_end = 0.05
self.eps_decay = 30000
self.gamma = 0.8
self.learning_rate = 0.001
self.batch_size = 512
self.memory = Memory(10000)
self.max_episodes = 10000
self.save_interval = 2
self.test_interval = 10
self.network_update_interval = 10
self.episode = -1
self.steps_done = 0
self.max_steps = 34
self.policy = DQN()
self.target = DQN()
self.test_network = DQN()
self.target.eval()
self.test_network.eval()
self.updateNetworks()
self.env = DroneEnv(useDepth)
self.optimizer = optim.Adam(self.policy.parameters(), self.learning_rate)
if torch.cuda.is_available():
print('Using device:', device)
print(torch.cuda.get_device_name(0))
else:
print("Using CPU")
# LOGGING
cwd = os.getcwd()
self.save_dir = os.path.join(cwd, "saved models")
if not os.path.exists(self.save_dir):
os.mkdir("saved models")
if not os.path.exists(os.path.join(cwd, "videos")):
os.mkdir("videos")
if torch.cuda.is_available():
self.policy = self.policy.to(device) # to use GPU
self.target = self.target.to(device) # to use GPU
self.test_network = self.test_network.to(device) # to use GPU
# model backup
files = glob.glob(self.save_dir + '\\*.pt')
if len(files) > 0:
files.sort(key=os.path.getmtime)
file = files[-1]
checkpoint = torch.load(file)
self.policy.load_state_dict(checkpoint['state_dict'])
self.episode = checkpoint['episode']
self.steps_done = checkpoint['steps_done']
self.updateNetworks()
print("Saved parameters loaded"
"\nModel: ", file,
"\nSteps done: ", self.steps_done,
"\nEpisode: ", self.episode)
else:
if os.path.exists("log.txt"):
open('log.txt', 'w').close()
if os.path.exists("last_episode.txt"):
open('last_episode.txt', 'w').close()
if os.path.exists("last_episode.txt"):
open('saved_model_params.txt', 'w').close()
self.optimizer = optim.Adam(self.policy.parameters(), self.learning_rate)
obs, _ = self.env.reset()
tensor = self.transformToTensor(obs)
writer.add_graph(self.policy, tensor)
def updateNetworks(self):
self.target.load_state_dict(self.policy.state_dict())
def transformToTensor(self, img):
tensor = torch.FloatTensor(img).to(device)
tensor = tensor.unsqueeze(0)
tensor = tensor.unsqueeze(0)
tensor = tensor.float()
return tensor
def convert_size(self, size_bytes):
if size_bytes == 0:
return "0B"
size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
i = int(math.floor(math.log(size_bytes, 1024)))
p = math.pow(1024, i)
s = round(size_bytes / p, 2)
return "%s %s" % (s, size_name[i])
def act(self, state):
self.eps_threshold = self.eps_end + (self.eps_start - self.eps_end) * math.exp(
-1.0 * self.steps_done / self.eps_decay
)
self.steps_done += 1
if random.random() > self.eps_threshold:
# print("greedy")
if torch.cuda.is_available():
action = np.argmax(self.policy(state).cpu().data.squeeze().numpy())
else:
action = np.argmax(self.policy(state).data.squeeze().numpy())
else:
action = random.randrange(0, 4)
return int(action)
def append_sample(self, state, action, reward, next_state):
next_state = self.transformToTensor(next_state)
current_q = self.policy(state).squeeze().cpu().detach().numpy()[action]
next_q = self.target(next_state).squeeze().cpu().detach().numpy()[action]
expected_q = reward + (self.gamma * next_q)
error = abs(current_q - expected_q),
self.memory.add(error, state, action, reward, next_state)
def learn(self):
if self.memory.tree.n_entries < self.batch_size:
return
states, actions, rewards, next_states, idxs, is_weights = self.memory.sample(self.batch_size)
states = tuple(states)
next_states = tuple(next_states)
states = torch.cat(states)
actions = np.asarray(actions)
rewards = np.asarray(rewards)
next_states = torch.cat(next_states)
current_q = self.policy(states)[[range(0, self.batch_size)], [actions]]
next_q =self.target(next_states).cpu().detach().numpy()[[range(0, self.batch_size)], [actions]]
expected_q = torch.FloatTensor(rewards + (self.gamma * next_q)).to(device)
errors = torch.abs(current_q.squeeze() - expected_q.squeeze()).cpu().detach().numpy()
# update priority
for i in range(self.batch_size):
idx = idxs[i]
self.memory.update(idx, errors[i])
loss = F.smooth_l1_loss(current_q.squeeze(), expected_q.squeeze())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
def train(self):
print("Starting...")
score_history = []
reward_history = []
if self.episode == -1:
self.episode = 1
for e in range(1, self.max_episodes + 1):
start = time.time()
state, _ = self.env.reset()
steps = 0
score = 0
while True:
state = self.transformToTensor(state)
action = self.act(state)
next_state, reward, done, _ = self.env.step(action)
if steps == self.max_steps:
done = 1
#self.memorize(state, action, reward, next_state)
self.append_sample(state, action, reward, next_state)
self.learn()
state = next_state
steps += 1
score += reward
if done:
print("----------------------------------------------------------------------------------------")
if self.memory.tree.n_entries < self.batch_size:
print("Training will start after ", self.batch_size - self.memory.tree.n_entries, " steps.")
break
print(
"episode:{0}, reward: {1}, mean reward: {2}, score: {3}, epsilon: {4}, total steps: {5}".format(
self.episode, reward, round(score / steps, 2), score, self.eps_threshold, self.steps_done))
score_history.append(score)
reward_history.append(reward)
with open('log.txt', 'a') as file:
file.write(
"episode:{0}, reward: {1}, mean reward: {2}, score: {3}, epsilon: {4}, total steps: {5}\n".format(
self.episode, reward, round(score / steps, 2), score, self.eps_threshold,
self.steps_done))
if torch.cuda.is_available():
print('Total Memory:', self.convert_size(torch.cuda.get_device_properties(0).total_memory))
print('Allocated Memory:', self.convert_size(torch.cuda.memory_allocated(0)))
print('Cached Memory:', self.convert_size(torch.cuda.memory_reserved(0)))
print('Free Memory:', self.convert_size(torch.cuda.get_device_properties(0).total_memory - (
torch.cuda.max_memory_allocated() + torch.cuda.max_memory_reserved())))
# tensorboard --logdir=runs
memory_usage_allocated = np.float64(round(torch.cuda.memory_allocated(0) / 1024 ** 3, 1))
memory_usage_cached = np.float64(round(torch.cuda.memory_reserved(0) / 1024 ** 3, 1))
writer.add_scalar("memory_usage_allocated", memory_usage_allocated, self.episode)
writer.add_scalar("memory_usage_cached", memory_usage_cached, self.episode)
writer.add_scalar('epsilon_value', self.eps_threshold, self.episode)
writer.add_scalar('score_history', score, self.episode)
writer.add_scalar('reward_history', reward, self.episode)
writer.add_scalar('Total steps', self.steps_done, self.episode)
writer.add_scalars('General Look', {'score_history': score,
'reward_history': reward}, self.episode)
# save checkpoint
if self.episode % self.save_interval == 0:
checkpoint = {
'episode': self.episode,
'steps_done': self.steps_done,
'state_dict': self.policy.state_dict()
}
torch.save(checkpoint, self.save_dir + '//EPISODE{}.pt'.format(self.episode))
if self.episode % self.network_update_interval == 0:
self.updateNetworks()
self.episode += 1
end = time.time()
stopWatch = end - start
print("Episode is done, episode time: ", stopWatch)
if self.episode % self.test_interval == 0:
self.test()
break
writer.close()
def test(self):
self.test_network.load_state_dict(self.target.state_dict())
start = time.time()
steps = 0
score = 0
image_array = []
state, next_state_image = self.env.reset()
image_array.append(next_state_image)
while True:
state = self.transformToTensor(state)
action = int(np.argmax(self.test_network(state).cpu().data.squeeze().numpy()))
next_state, reward, done, next_state_image = self.env.step(action)
image_array.append(next_state_image)
if steps == self.max_steps:
done = 1
state = next_state
steps += 1
score += reward
if done:
print("----------------------------------------------------------------------------------------")
print("TEST, reward: {}, score: {}, total steps: {}".format(
reward, score, self.steps_done))
with open('tests.txt', 'a') as file:
file.write("TEST, reward: {}, score: {}, total steps: {}\n".format(
reward, score, self.steps_done))
writer.add_scalars('Test', {'score': score, 'reward': reward}, self.episode)
end = time.time()
stopWatch = end - start
print("Test is done, test time: ", stopWatch)
# Convert images to video
frameSize = (256, 144)
import cv2
video = cv2.VideoWriter("videos\\test_video_episode_{}_score_{}.avi".format(self.episode, score), cv2.VideoWriter_fourcc(*'DIVX'), 7, frameSize)
for img in image_array:
video.write(img)
video.release()
break