用深度强化学习来玩Flappy Bird

目录

演示视频

核心代码

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演示视频

用深度强化学习来玩Flappy Bird

核心代码

import torch.nn as nnclass 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 output

from 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 npclass FlappyBird(object):init()fps_clock = time.Clock()screen_width = 288screen_height = 512screen = 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 = 30pipe_gap_size = 100pipe_velocity_x = -4# parameters for birdmin_velocity_y = -8max_velocity_y = 10downward_speed = 1upward_speed = -9bird_index_generator = cycle([0, 1, 2, 1])def __init__(self):self.iter = self.bird_index = self.score = 0self.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 = 0self.base_y = self.screen_height * 0.79self.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_widthpipes[1]["x_upper"] = pipes[1]["x_lower"] = self.screen_width * 1.5self.pipes = pipesself.current_velocity_y = 0self.is_flapped = Falsedef generate_pipe(self):x = self.screen_width + 10gap_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 groundif self.bird_height + self.bird_y + 1 >= self.base_y:return Truebird_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 pipeif bird_bbox.collidelist(pipe_boxes) == -1:return Falsefor i in range(2):cropped_bbox = bird_bbox.clip(pipe_boxes[i])min_x1 = cropped_bbox.x - bird_bbox.xmin_y1 = cropped_bbox.y - bird_bbox.ymin_x2 = cropped_bbox.x - pipe_boxes[i].xmin_y2 = cropped_bbox.y - pipe_boxes[i].yif 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 Truereturn Falsedef next_frame(self, action):pump()reward = 0.1terminal = False# Check input actionif action == 1:self.current_velocity_y = self.upward_speedself.is_flapped = True# Update scorebird_center_x = self.bird_x + self.bird_width / 2for pipe in self.pipes:pipe_center_x = pipe["x_upper"] + self.pipe_width / 2if pipe_center_x < bird_center_x < pipe_center_x + 5:self.score += 1reward = 1break# Update index and iterationif (self.iter + 1) % 3 == 0:self.bird_index = next(self.bird_index_generator)self.iter = 0self.base_x = -((-self.base_x + 100) % self.base_shift)# Update bird's positionif self.current_velocity_y < self.max_velocity_y and not self.is_flapped:self.current_velocity_y += self.downward_speedif self.is_flapped:self.is_flapped = Falseself.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' positionfor pipe in self.pipes:pipe["x_upper"] += self.pipe_velocity_xpipe["x_lower"] += self.pipe_velocity_x# Update pipesif 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 = Truereward = -1self.__init__()# Draw everythingself.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 torchfrom src.deep_q_network import DeepQNetwork
from src.flappy_bird import FlappyBird
from src.utils import pre_processingdef 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 argsdef 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_stateif __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 argsdef 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 = 0while iter < opt.num_iters:prediction = model(state)[0]# Exploration or exploitationepsilon = opt.final_epsilon + ((opt.num_iters - iter) * (opt.initial_epsilon - opt.final_epsilon) / opt.num_iters)u = random()random_action = u <= epsilonif 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 inzip(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_stateiter += 1print("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)