用深度强化学习来玩Flappy Bird
目录
演示视频
核心代码
演示视频
用深度强化学习来玩Flappy Bird
核心代码
import torch.nn as nn
class DeepQNetwork(nn.Module):
def __init__(self):
super(DeepQNetwork, self).__init__()
self.conv1 = nn.Sequential(nn.Conv2d(4, 32, kernel_size=8, stride=4), nn.ReLU(inplace=True))
self.conv2 = nn.Sequential(nn.Conv2d(32, 64, kernel_size=4, stride=2), nn.ReLU(inplace=True))
self.conv3 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, stride=1), nn.ReLU(inplace=True))
self.fc1 = nn.Sequential(nn.Linear(7 * 7 * 64, 512), nn.ReLU(inplace=True))
self.fc2 = nn.Linear(512, 2)
self._create_weights()
def _create_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
nn.init.uniform_(m.weight, -0.01, 0.01)
nn.init.constant_(m.bias, 0)
def forward(self, input):
output = self.conv1(input)
output = self.conv2(output)
output = self.conv3(output)
output = output.view(output.size(0), -1)
output = self.fc1(output)
output = self.fc2(output)
return outputfrom itertools import cycle
from numpy.random import randint
from pygame import Rect, init, time, display
from pygame.event import pump
from pygame.image import load
from pygame.surfarray import array3d, pixels_alpha
from pygame.transform import rotate
import numpy as np
class FlappyBird(object):
init()
fps_clock = time.Clock()
screen_width = 288
screen_height = 512
screen = display.set_mode((screen_width, screen_height))
display.set_caption('Deep Q-Network Flappy Bird')
base_image = load('assets/sprites/base.png').convert_alpha()
background_image = load('assets/sprites/background-black.png').convert()
pipe_images = [rotate(load('assets/sprites/pipe-green.png').convert_alpha(), 180),
load('assets/sprites/pipe-green.png').convert_alpha()]
bird_images = [load('assets/sprites/redbird-upflap.png').convert_alpha(),
load('assets/sprites/redbird-midflap.png').convert_alpha(),
load('assets/sprites/redbird-downflap.png').convert_alpha()]
# number_images = [load('assets/sprites/{}.png'.format(i)).convert_alpha() for i in range(10)]
bird_hitmask = [pixels_alpha(image).astype(bool) for image in bird_images]
pipe_hitmask = [pixels_alpha(image).astype(bool) for image in pipe_images]
fps = 30
pipe_gap_size = 100
pipe_velocity_x = -4
# parameters for bird
min_velocity_y = -8
max_velocity_y = 10
downward_speed = 1
upward_speed = -9
bird_index_generator = cycle([0, 1, 2, 1])
def __init__(self):
self.iter = self.bird_index = self.score = 0
self.bird_width = self.bird_images[0].get_width()
self.bird_height = self.bird_images[0].get_height()
self.pipe_width = self.pipe_images[0].get_width()
self.pipe_height = self.pipe_images[0].get_height()
self.bird_x = int(self.screen_width / 5)
self.bird_y = int((self.screen_height - self.bird_height) / 2)
self.base_x = 0
self.base_y = self.screen_height * 0.79
self.base_shift = self.base_image.get_width() - self.background_image.get_width()
pipes = [self.generate_pipe(), self.generate_pipe()]
pipes[0]["x_upper"] = pipes[0]["x_lower"] = self.screen_width
pipes[1]["x_upper"] = pipes[1]["x_lower"] = self.screen_width * 1.5
self.pipes = pipes
self.current_velocity_y = 0
self.is_flapped = False
def generate_pipe(self):
x = self.screen_width + 10
gap_y = randint(2, 10) * 10 + int(self.base_y / 5)
return {"x_upper": x, "y_upper": gap_y - self.pipe_height, "x_lower": x, "y_lower": gap_y + self.pipe_gap_size}
def is_collided(self):
# Check if the bird touch ground
if self.bird_height + self.bird_y + 1 >= self.base_y:
return True
bird_bbox = Rect(self.bird_x, self.bird_y, self.bird_width, self.bird_height)
pipe_boxes = []
for pipe in self.pipes:
pipe_boxes.append(Rect(pipe["x_upper"], pipe["y_upper"], self.pipe_width, self.pipe_height))
pipe_boxes.append(Rect(pipe["x_lower"], pipe["y_lower"], self.pipe_width, self.pipe_height))
# Check if the bird's bounding box overlaps to the bounding box of any pipe
if bird_bbox.collidelist(pipe_boxes) == -1:
return False
for i in range(2):
cropped_bbox = bird_bbox.clip(pipe_boxes[i])
min_x1 = cropped_bbox.x - bird_bbox.x
min_y1 = cropped_bbox.y - bird_bbox.y
min_x2 = cropped_bbox.x - pipe_boxes[i].x
min_y2 = cropped_bbox.y - pipe_boxes[i].y
if np.any(self.bird_hitmask[self.bird_index][min_x1:min_x1 + cropped_bbox.width,
min_y1:min_y1 + cropped_bbox.height] * self.pipe_hitmask[i][min_x2:min_x2 + cropped_bbox.width,
min_y2:min_y2 + cropped_bbox.height]):
return True
return False
def next_frame(self, action):
pump()
reward = 0.1
terminal = False
# Check input action
if action == 1:
self.current_velocity_y = self.upward_speed
self.is_flapped = True
# Update score
bird_center_x = self.bird_x + self.bird_width / 2
for pipe in self.pipes:
pipe_center_x = pipe["x_upper"] + self.pipe_width / 2
if pipe_center_x < bird_center_x < pipe_center_x + 5:
self.score += 1
reward = 1
break
# Update index and iteration
if (self.iter + 1) % 3 == 0:
self.bird_index = next(self.bird_index_generator)
self.iter = 0
self.base_x = -((-self.base_x + 100) % self.base_shift)
# Update bird's position
if self.current_velocity_y < self.max_velocity_y and not self.is_flapped:
self.current_velocity_y += self.downward_speed
if self.is_flapped:
self.is_flapped = False
self.bird_y += min(self.current_velocity_y, self.bird_y - self.current_velocity_y - self.bird_height)
if self.bird_y < 0:
self.bird_y = 0
# Update pipes' position
for pipe in self.pipes:
pipe["x_upper"] += self.pipe_velocity_x
pipe["x_lower"] += self.pipe_velocity_x
# Update pipes
if 0 < self.pipes[0]["x_lower"] < 5:
self.pipes.append(self.generate_pipe())
if self.pipes[0]["x_lower"] < -self.pipe_width:
del self.pipes[0]
if self.is_collided():
terminal = True
reward = -1
self.__init__()
# Draw everything
self.screen.blit(self.background_image, (0, 0))
self.screen.blit(self.base_image, (self.base_x, self.base_y))
self.screen.blit(self.bird_images[self.bird_index], (self.bird_x, self.bird_y))
for pipe in self.pipes:
self.screen.blit(self.pipe_images[0], (pipe["x_upper"], pipe["y_upper"]))
self.screen.blit(self.pipe_images[1], (pipe["x_lower"], pipe["y_lower"]))
image = array3d(display.get_surface())
display.update()
self.fps_clock.tick(self.fps)
return image, reward, terminal
import argparse
import torch
from src.deep_q_network import DeepQNetwork
from src.flappy_bird import FlappyBird
from src.utils import pre_processing
def get_args():
parser = argparse.ArgumentParser(
"""Implementation of Deep Q Network to play Flappy Bird""")
parser.add_argument("--image_size", type=int, default=84, help="The common width and height for all images")
parser.add_argument("--saved_path", type=str, default="trained_models")
args = parser.parse_args()
return args
def q_test(opt):
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
if torch.cuda.is_available():
model = torch.load("{}/flappy_bird".format(opt.saved_path))
else:
model = torch.load("{}/flappy_bird".format(opt.saved_path), map_location=lambda storage, loc: storage)
model.eval()
game_state = FlappyBird()
image, reward, terminal = game_state.next_frame(0)
image = pre_processing(image[:game_state.screen_width, :int(game_state.base_y)], opt.image_size, opt.image_size)
image = torch.from_numpy(image)
if torch.cuda.is_available():
model.cuda()
image = image.cuda()
state = torch.cat(tuple(image for _ in range(4)))[None, :, :, :]
while True:
prediction = model(state)[0]
action = torch.argmax(prediction)
next_image, reward, terminal = game_state.next_frame(action)
next_image = pre_processing(next_image[:game_state.screen_width, :int(game_state.base_y)], opt.image_size,
opt.image_size)
next_image = torch.from_numpy(next_image)
if torch.cuda.is_available():
next_image = next_image.cuda()
next_state = torch.cat((state[0, 1:, :, :], next_image))[None, :, :, :]
state = next_state
if __name__ == "__main__":
opt = get_args()
q_test(opt)
def get_args():
parser = argparse.ArgumentParser(
"""Implementation of Deep Q Network to play Flappy Bird""")
parser.add_argument("--image_size", type=int, default=84, help="The common width and height for all images")
parser.add_argument("--batch_size", type=int, default=32, help="The number of images per batch")
parser.add_argument("--optimizer", type=str, choices=["sgd", "adam"], default="adam")
parser.add_argument("--lr", type=float, default=1e-6)
parser.add_argument("--gamma", type=float, default=0.99)
parser.add_argument("--initial_epsilon", type=float, default=0.1)
parser.add_argument("--final_epsilon", type=float, default=1e-4)
parser.add_argument("--num_iters", type=int, default=2000000)
parser.add_argument("--replay_memory_size", type=int, default=50000,
help="Number of epoches between testing phases")
parser.add_argument("--log_path", type=str, default="tensorboard")
parser.add_argument("--saved_path", type=str, default="trained_models")
args = parser.parse_args()
return args
def train(opt):
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
model = DeepQNetwork()
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
writer = SummaryWriter(opt.log_path)
optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
criterion = nn.MSELoss()
game_state = FlappyBird()
image, reward, terminal = game_state.next_frame(0)
image = pre_processing(image[:game_state.screen_width, :int(game_state.base_y)], opt.image_size, opt.image_size)
image = torch.from_numpy(image)
if torch.cuda.is_available():
model.cuda()
image = image.cuda()
state = torch.cat(tuple(image for _ in range(4)))[None, :, :, :]
replay_memory = []
iter = 0
while iter < opt.num_iters:
prediction = model(state)[0]
# Exploration or exploitation
epsilon = opt.final_epsilon + (
(opt.num_iters - iter) * (opt.initial_epsilon - opt.final_epsilon) / opt.num_iters)
u = random()
random_action = u <= epsilon
if random_action:
print("Perform a random action")
action = randint(0, 1)
else:
action = torch.argmax(prediction)
next_image, reward, terminal = game_state.next_frame(action)
next_image = pre_processing(next_image[:game_state.screen_width, :int(game_state.base_y)], opt.image_size,
opt.image_size)
next_image = torch.from_numpy(next_image)
if torch.cuda.is_available():
next_image = next_image.cuda()
next_state = torch.cat((state[0, 1:, :, :], next_image))[None, :, :, :]
replay_memory.append([state, action, reward, next_state, terminal])
if len(replay_memory) > opt.replay_memory_size:
del replay_memory[0]
batch = sample(replay_memory, min(len(replay_memory), opt.batch_size))
state_batch, action_batch, reward_batch, next_state_batch, terminal_batch = zip(*batch)
state_batch = torch.cat(tuple(state for state in state_batch))
action_batch = torch.from_numpy(
np.array([[1, 0] if action == 0 else [0, 1] for action in action_batch], dtype=np.float32))
reward_batch = torch.from_numpy(np.array(reward_batch, dtype=np.float32)[:, None])
next_state_batch = torch.cat(tuple(state for state in next_state_batch))
if torch.cuda.is_available():
state_batch = state_batch.cuda()
action_batch = action_batch.cuda()
reward_batch = reward_batch.cuda()
next_state_batch = next_state_batch.cuda()
current_prediction_batch = model(state_batch)
next_prediction_batch = model(next_state_batch)
y_batch = torch.cat(
tuple(reward if terminal else reward + opt.gamma * torch.max(prediction) for reward, terminal, prediction in
zip(reward_batch, terminal_batch, next_prediction_batch)))
q_value = torch.sum(current_prediction_batch * action_batch, dim=1)
optimizer.zero_grad()
# y_batch = y_batch.detach()
loss = criterion(q_value, y_batch)
loss.backward()
optimizer.step()
state = next_state
iter += 1
print("Iteration: {}/{}, Action: {}, Loss: {}, Epsilon {}, Reward: {}, Q-value: {}".format(
iter + 1,
opt.num_iters,
action,
loss,
epsilon, reward, torch.max(prediction)))
writer.add_scalar('Train/Loss', loss, iter)
writer.add_scalar('Train/Epsilon', epsilon, iter)
writer.add_scalar('Train/Reward', reward, iter)
writer.add_scalar('Train/Q-value', torch.max(prediction), iter)
if (iter+1) % 1000000 == 0:
torch.save(model, "{}/flappy_bird_{}".format(opt.saved_path, iter+1))
torch.save(model, "{}/flappy_bird".format(opt.saved_path))
if __name__ == "__main__":
opt = get_args()
train(opt)