def playFlappyBird():
# Step 1: init BrainDQN
actions = 2
brain = BrainDQN(actions)
# Step 2: init Flappy Bird Game
flappyBird = game.GameState()
# Step 3: play game
# Step 3.1: obtain init state
action0 = np.array([1, 0]) # do nothing
observation0, reward0, terminal = flappyBird.frame_step(action0)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)),
cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0, 1, 255, cv2.THRESH_BINARY)
print observation0.shape
brain.setInitState(observation0)
# Step 3.2: run the game
while 1 != 0:
action = brain.getAction()
print action
nextObservation, reward, terminal = flappyBird.frame_step(action)
nextObservation = preprocess(nextObservation)
brain.setPerception(nextObservation, action, reward, terminal)
cv2.imshow('Video', 0)
if cv2.waitKey(1) & 0xFF == 27:
break
def run_game():
game = Game()
RL = BrainDQN(actions=8)
step = 0
for episode in range(500):
# initial observation
observation = game.reset_map()
# print(observation.shape)
RL.setInitState(observation)
while True:
# RL choose action based on observation
action = RL.getAction()
# for i in range(6):
# print(game.states[:,:,i])
# print("action:", action)
# RL take action and get next observation and reward
observation_, reward, done = game.step(action)
# for i in range(6):
# print(game.states[:,:,i])
# print("reward:",reward)
# print("done:", done)
RL.setPerception(observation_, action, reward, done)
# break while loop when end of this episode
if done:
# print("done,reset")
observation = game.reset_map()
RL.setInitState(observation)
def playSnake():
# Step 1: init BrainDQN
actions = 4
top = 0
brain = BrainDQN(actions)
# Step 2: init Plane Game
GluttonousSnake = game.GameState()
# Step 3: play game
# Step 3.1: obtain init state
action0 = np.array([0, 0, 0,
1]) # [1,0,0]do nothing,[0,1,0]left,[0,0,1]right
observation0, reward0, terminal, score = GluttonousSnake.frame_step(
action0)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)),
cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0, 1, 255, cv2.THRESH_BINARY)
# cv2.imwrite("3.jpg",observation0,[int(cv2.IMWRITE_JPEG_QUALITY),5])
brain.setInitState(observation0)
# Step 3.2: run the game
while 1 != 0:
action = brain.getAction()
nextObservation, reward, terminal, score = GluttonousSnake.frame_step(
action)
nextObservation = preprocess(nextObservation)
brain.setPerception(nextObservation, action, reward, terminal)
if score > top:
top = score
print('top:%u' % top)
def playFlappyBird():
# Step 1: init BrainDQN
actions = 2
brain = BrainDQN(actions)
# Step 2: init Flappy Bird Game
flappyBird = game.GameState()
# Step 3: play game
# Step 3.1: obtain init state
action0 = np.array([1, 0]) # do nothing
observation0, reward0, terminal = flappyBird.frame_step(action0)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)),
cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0, 1, 255, cv2.THRESH_BINARY)
brain.setInitState(observation0)
# Step 3.2: run the game
while 1 != 0:
action = brain.getAction() # 对初始的状态有action反馈 at
nextObservation, reward, terminal = flappyBird.frame_step(
action) # 执行器获得指令，并输出该指令的奖励r(t),执行该指令导致的观测 o(t+1)，
nextObservation = preprocess(nextObservation)
tmp_img = showThreshImg(nextObservation) # 从flappyBird中得到的图像是一个旋转加镜像的
cv2.imshow("process", tmp_img)
cv2.waitKey(1)
brain.setPerception(nextObservation, action, reward, terminal)
def HighSpeedRacing():
# Step 1: init BrainDQN
actions = 5
brain = BrainDQN(actions, imgDim)
# Step 2: init Flappy Bird Game
flappyBird = game.GameState()
# Step 3: play game
# Step 3.1: obtain init state
action0 = np.array([0, 1, 0, 0, 0]) # do nothing
observation0, reward0, terminal = flappyBird.frame_step(action0)
print(observation0)
# print('observation0 1:',observation0)
# observation0 = cv2.cvtColor(cv2.resize(observation0, (imgDim[0],imgDim[1])), cv2.COLOR_BGR2GRAY)
# ret, observation0 = cv2.threshold(observation0,1,255,cv2.THRESH_BINARY)
brain.setInitState(
observation0,
action0) #将observation0复制4份放进BrainDQN的属性self.currentState中
# isUseExpertData = False
## isUseExpertData = True
# if(isUseExpertData == True):
# filename = "./expertData/observation"
# actInd = 0
# observation0 = np.load(filename + str(actInd) + ".npy")
# plt.imshow(observation0)
# # # Step 3.2: run the game
# # while 1!= 0:
# for _ in range(1):
# actInd = 0
# for actInd in range(1,2073):
# actInd += 1
# action = np.load(filename + "action" + str(actInd) + ".npy")
# reward = np.load(filename + "reward" + str(actInd) + ".npy")
# terminal = np.load(filename + "terminal" + str(actInd) + ".npy")
# nextObservation = np.load(filename + str(actInd) + ".npy")
# plt.imshow(nextObservation)
# nextObservation = preprocess(nextObservation)
# brain.setPerception(nextObservation,action,reward,terminal)
loss = []
plt.figure()
ind = 0
# Step 3.2: run the game
while 1 != 0:
# time.sleep(0.1)
action = brain.getAction()
loss.append(brain.loss_temp)
ind += 1
if ind % 500 == 499:
plt.plot(loss)
plt.show()
nextObservation, reward, terminal = flappyBird.frame_step(action)
# nextObservation = preprocess(nextObservation)
brain.setPerception(nextObservation, action, reward, terminal)
def main():
env = gym.make(ENV_NAME)
agent = BrainDQN(env.action_space.n)
try:
for episode in range(EPISODE):
state = env.reset()
state = ImageProcess.reshapeHalf(state)
agent.setInitState(state)
life = 5
for step in range(MAX_STEP):
# env.render()
action = agent.getAction()
next_state, reward, done, info = env.step(action)
next_state = np.reshape(ImageProcess.reshapeHalf(next_state),
(80, 80, 1))
if info['ale.lives'] < life:
#reward-=(life-info['ale.lives'])
life = info['ale.lives']
agent.setPerception(next_state, action, reward, done, episode)
if done:
break
if episode % 500 == 100:
agent.e_greedy = False
for e in range(10):
state = env.reset()
state = ImageProcess.reshapeHalf(state)
agent.setInitState(state)
life = 5
for step in range(MAX_STEP):
# env.render()
action = agent.getAction()
next_state, reward, done, info = env.step(action)
next_state = np.reshape(
ImageProcess.reshapeHalf(next_state), (80, 80, 1))
if info['ale.lives'] < life:
#reward-=(life-info['ale.lives'])
life = info['ale.lives']
agent.setPerception(next_state, action, reward, done,
episode)
if done:
break
agent.e_greedy = True
finally:
pass
def playFlappyBird():
actions = 2
brain = BrainDQN(actions)
flappyBird = game.GameState()
action0 = np.array([1, 0])
observation0, reward0, terminal = flappyBird.frame_step(action0)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)),
cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0, 1, 255, cv2.THRESH_BINARY)
brain.setInitState(observation0)
while 1 != 0:
action = brain.getAction()
nextObservation, reward, terminal = flappyBird.frame_step(action)
nextObservation = preprocess(nextObservation)
brain.setPerception(nextObservation, action, reward, terminal)
def playGame():
game = GameObject()
game.train = TRAIN
actions = 3
brain = BrainDQN(actions, game)
action0 = np.array([1, 0, 0]) # do nothing
observation0, _ = game.frame_step(action0)
observation0 = cv2.resize(observation0, (IMG_HEIGHT, IMG_WIDTH))
observation0 = cv2.cvtColor(observation0, cv2.COLOR_BGR2GRAY)
brain.setInitState(observation0)
while True:
action = brain.getAction()
nextObservation, reward = game.frame_step(action)
nextObservation = preprocess(nextObservation)
brain.setPerception(nextObservation, action, reward)
def playGame():
# Step 0: Define reort
win = 0
lose = 0
points = 0
# Step 1: init BrainDQN
actions = 5
brain = BrainDQN(actions)
# Step 2: init Game
bg = Game()
# Step 3: play game
# Step 3.1: obtain init state
action0 = bg.gen_action([1, 0, 0, 0, 0]) # do nothing
observation0, reward0, terminal = bg.step(action0)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)),
cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0, 1, 255, cv2.THRESH_BINARY)
brain.setInitState(observation0)
# Step 3.2: run the game
while True:
pygame.event.get() #讓遊戲畫面能夠更新
action = bg.gen_action(brain.getAction())
Observation, reward, terminal = bg.step(action)
nextObservation = preprocess(Observation)
brain.setPerception(nextObservation, action, reward, terminal)
######################## 統計輸出報表用 ########################
points += reward
if terminal == True:
win += points
lose += 1
points = 0
bg = Game()
print("Lost cnt:", lose, " ,win_points:", round(points, 2), " ,cnt:",
brain.timeStep)
if brain.timeStep % 1000 == 0:
learn_rate.append(lose)
win_cnt.append(win)
plt.plot(learn_rate, "g")
plt.plot(win_cnt, "r")
plt.show()
lose = 0
win = 0
def PlaySurvalNew():
logging.basicConfig(filename='logs/myplayer.log', level=logging.INFO)
logging.info('Started')
# 9个方向
action_space = ['u', 'd', 'l', 'r', 'a', 'x', 's', 'w', 'e']
n_actions = len(action_space)
train = BrainDQN(n_actions)
# 初始化随机地图类
genermap = map()
TrainGame = Game()
mapnum = genermap.init_battle_map()
for i in range(mapnum):
#计算每张地图循环多少次
looptimes = genermap.calclooptimes(i)
logging.info('loop %d,i %d', looptimes, i)
for j in range(looptimes):
flag = False
index = j
if j >= looptimes / 2:
flag = True
index = j - looptimes
# 初始化随机地图信息
reservelist = genermap.fillplayerpositon(index, i, flag)
# 产生毒气
posionlist = genermap.GeneratorPosion(i)
# 产生道具
genermap.GeneratorTool(reservelist, posionlist, i)
# 初始化Game环境
TrainGame.binary_env_reset(genermap.cacheMap, genermap.PosionMap)
train.setInitState(TrainGame.binary_env)
# 循环训练1W次，换一张地图
for episode in range(5000):
# 随机取一个方向
action = train.getAction()
# 计算该方向的reward
nextObservation, reward, terminal = TrainGame.binary_step(
action)
# 设置到训练集
train.setPerception(nextObservation, action, reward, terminal)
if terminal == True:
TrainGame.binary_env_reset(genermap.cacheMap,
genermap.PosionMap)
train.setInitState(TrainGame.binary_env)
def playFlappyBird(): # Step 1: init BrainDQN actions = 2 brain = BrainDQN(actions) # Step 2: init Flappy Bird Game flappyBird = game.GameState() # Step 3: play game # Step 3.1: obtain init state action0 = np.array([1,0]) # do nothing observation0, reward0, terminal = flappyBird.frame_step(action0) observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)), cv2.COLOR_BGR2GRAY) ret, observation0 = cv2.threshold(observation0,1,255,cv2.THRESH_BINARY) brain.setInitState(observation0) # Step 3.2: run the game while 1!= 0: action = brain.getAction() nextObservation,reward,terminal = flappyBird.frame_step(action) nextObservation = preprocess(nextObservation) brain.setPerception(nextObservation,action,reward,terminal)
def playSurvival():
logging.basicConfig(filename='logs/myplayer.log', level=logging.INFO)
logging.info('Started')
# 9个方向
action_space = ['u', 'd', 'l', 'r', 'a', 'x', 's', 'w', 'e']
n_actions = len(action_space)
train = BrainDQN(n_actions)
# 初始化随机地图类
genermap = map()
TrainGame = Game()
for i in range(6000000):
loop = 0
loop += int(i / 1000000)
print("loop:", loop)
logging.info('loop %d,i %d', loop, i)
# 初始化随机地图信息
genermap.init_battle_map()
reservelist = genermap.fillramdomplayer(loop)
print("Posion start")
#产生毒气
posionlist = genermap.GeneratorPosion(i)
print("Posion end")
#产生道具
genermap.GeneratorTool(reservelist, posionlist, i)
# 初始化Game环境
TrainGame.binary_env_reset(genermap.cacheMap, genermap.PosionMap)
train.setInitState(TrainGame.binary_env)
# 循环训练1W次，换一张地图
for episode in range(2000):
# 随机取一个方向
action = train.getAction()
# 计算该方向的reward
nextObservation, reward, terminal = TrainGame.binary_step(action)
# 设置到训练集
train.setPerception(nextObservation, action, reward, terminal)
if terminal == True:
TrainGame.binary_env_reset(genermap.cacheMap,
genermap.PosionMap)
train.setInitState(TrainGame.binary_env)
break
def playPlane():
# Step 1: init BrainDQN
actions = 3
brain = BrainDQN(actions)
# Step 2: init Plane Game
plane = game.GameState()
# Step 3: play game
# Step 3.1: obtain init state
action0 = np.array([1, 0, 0]) # [1,0,0]do nothing,[0,1,0]left,[0,0,1]right
observation0, reward0, terminal = plane.frame_step(action0)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)),
cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0, 1, 255, cv2.THRESH_BINARY)
brain.setInitState(observation0)
# Step 3.2: run the game
while 1 != 0:
action = brain.getAction()
nextObservation, reward, terminal = plane.frame_step(action)
nextObservation = preprocess(nextObservation)
brain.setPerception(nextObservation, action, reward, terminal)
def playFlappyBird():
# Step 1: init BrainDQN
actions = 2
brain = BrainDQN(actions)
# Step 2: init Flappy Bird Game
flappyBird = game.GameState()
# Step 3: play game
# Step 3.1: obtain init state
action0 = np.array([1, 0]) # do nothing
observation0, reward0, terminal = flappyBird.frame_step(action0)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)), cv2.COLOR_BGR2GRAY)
_, observation0 = cv2.threshold(observation0, 1, 255, cv2.THRESH_BINARY)
# observation0 = preprocess(observation0) # 为何初始化brain时使用的图片是[80,80],而不是[80,80,1] => 因为setInitState（）输入的是4张重叠的图片
brain.setInitState(observation0)
# Step 3.2: run the game
while True:
action = brain.getAction() # 基于当前observation获取下一个action
nextObservation, reward, terminal = flappyBird.frame_step(action) # 采取行动action
nextObservation = preprocess(nextObservation)
brain.setPerception(nextObservation, action, reward, terminal) # 学习这个行动带来的回报
def playFlappyBird():
# Step 1: init BrainDQN
actions = 2
brain = BrainDQN(actions)
# Step 2: init Flappy Bird Game
flappyBird = game.GameState()
flappyBird.reset_max_score()
# Step 3: play game
# Step 3.1: obtain init state
action0 = np.array([1,0]) # do nothing
observation0, reward0, terminal = flappyBird.frame_step(action0)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)), cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0,1,255,cv2.THRESH_BINARY)
brain.setInitState(observation0)
# Step 3.2: run the game
while 1!= 0:
action = brain.getAction()
nextObservation,reward,terminal = flappyBird.frame_step(action)
nextObservation = preprocess(nextObservation)
brain.setPerception(nextObservation,action,reward,terminal)
# "/ STATE", brain.state
print("TS {}/AVG {:.2f}/EPS {:.4f}/MAX {}/TURN {}".format(brain.timeStep,
flappyBird.avg_score, brain.epsilon, flappyBird.max_score, flappyBird.turns))
def playFlappyBird():
# Step 1: init BrainDQN
actions = 2
brain = BrainDQN(actions)
# Step 2: init Flappy Bird Game
flappyBird = game.GameState()
# Step 3: play game
# Step 3.1: obtain init state
action0 = np.array([1,0]) # do nothing
observation0, reward0, terminal = flappyBird.frame_step(action0)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)), cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0,1,255,cv2.THRESH_BINARY)
brain.setInitState(observation0.astype(np.float32))
# Step 3.2: run the game
writer = tf.summary.create_file_writer("tf_logs")
with writer.as_default():
while 1!= 0:
action = brain.getAction()
nextObservation,reward,terminal = flappyBird.frame_step(action)
nextObservation = preprocess(nextObservation)
step, loss = brain.setPerception(nextObservation,action,reward,terminal)
tf.summary.scalar("loss", loss, step=step)
writer.flush()
if action_pl[0] == 1.0 and action_pl[3] == 0:
action_nerual = [1.0, 0, 0, 0, 0, 0, 0]
elif action_pl[0] == 1.0 and action_pl[3] == 1:
action_nerual = [0, 1.0, 0, 0, 0, 0, 0]
elif action_pl[0] == 1.0 and action_pl[3] == 2:
action_nerual = [0, 0, 1.0, 0, 0, 0, 0]
elif action_pl[0] == -1.0 and action_pl[1] == 0:
action_nerual = [0, 0, 0, 1.0, 0, 0, 0]
elif action_pl[0] == -1.0 and action_pl[1] == 90:
action_nerual = [0, 0, 0, 0, 1.0, 0, 0]
elif action_pl[0] == -1.0 and action_pl[1] == 180:
action_nerual = [0, 0, 0, 0, 0, 1.0, 0]
else:
action_nerual = [0, 0, 0, 0, 0, 0, 1.0]
brain.setPerception(obs, action_nerual, reward, done)
#unit_myself_index += 1
#print(action,obs,reward,done,info)
episodes += 1
#obs = env.reset()
_, p_state = env.reset()
#test
total_reward = 0
avg_reward = 0
TEST_EPISODE = 50
if episodes % 2000 == 0:
endtime = time.time()
gtime = endtime - startime
print gtime
for test_i in xrange(TEST_EPISODE):
def TestTrainer():
init()
screen = display.set_mode((WIDTH, HEIGHT))
surf = Surface((WIDTH, HEIGHT))
trainerBinding = Trainer(surf)
# start game, create DQN
#trainerBinding.initializeGame("ASDFASDF",startAt=Trainer.START_AT_MONSTER_ROOM)
start(trainerBinding)
clock = time.Clock()
actions = 8
brain = BrainDQN(actions)
actionList = [
Trainer.MOVE_LEFT, Trainer.MOVE_RIGHT, Trainer.MOVE_UP,
Trainer.MOVE_DOWN, Trainer.SHOOT_LEFT, Trainer.SHOOT_RIGHT,
Trainer.SHOOT_UP, Trainer.SHOOT_DOWN
]
# initialize first state
action0 = np.array([1, 0, 0, 0, 0, 0, 0, 0])
trainerBinding.sendPushKeyEvent(actionList[np.argmax(action0)])
frameData0 = trainerBinding.advanceFrame()
observation0 = surfarray.array3d(frameData0.surface)
observation0 = cv2.cvtColor(cv2.resize(observation0, (80, 80)),
cv2.COLOR_BGR2GRAY)
ret, observation0 = cv2.threshold(observation0, 1, 255, cv2.THRESH_BINARY)
brain.setInitState(observation0)
lastIsaacHP = frameData0.isaac_hp
lastNumEnemies = frameData0.num_enemies
#start trainings
running = True
score = 0
epochs = 0
winEpoch = 0
# Initialize variables used for visualizations
totalReward = 0
numQValues = 0
totalQValue = 0
while running:
# print current epoch and score
print("epoch", epochs, "/ score", score)
# a harness for computer input, detects when the x
# on the window has been clicked and exits accordingly
for evt in event.get():
if evt.type == QUIT:
print("first win epoch", winEpoch)
running = False
# get action from the DQN
action, QValue = brain.getAction()
totalQValue += QValue
numQValues += 1
currentAction = actionList[np.argmax(action)]
trainerBinding.sendPushKeyEvent(currentAction)
# advance the simulation
frameData = trainerBinding.advanceFrame()
# Record position
pos = np.array(frameData.isaac_position)
positions.append(np.round(pos, decimals=2))
# if enter another room, restart
if frameData.has_room_changed:
start(trainerBinding)
# get hp
currentIsaacHP = frameData.isaac_hp
currentNumEnemies = frameData.num_enemies
# if boss is dead, restart game and update score and epochs
if currentNumEnemies == 0:
score = score + 1
epochs = epochs + 1
if score == 1:
winEpoch = epochs
totalRewardsInEpochs.append(totalReward)
totalReward = 0
averageQValuesInEpochs.append(totalQValue / numQValues)
totalQValue = 0
numQValues = 0
start(trainerBinding)
#trainerBinding.initializeGame("ASDFASDF",startAt=Trainer.START_AT_MONSTER_ROOM)
# get reward
reward = getReward(currentIsaacHP, lastIsaacHP, currentNumEnemies,
lastNumEnemies)
totalReward += reward
lastIsaacHP = currentIsaacHP
lastNumEnemies = currentNumEnemies
# get terminal state, if terminal, restart game and update epochs
terminal = terminalState(trainerBinding.getSimulationStatus())
if terminal == True: #or reward == -1:
epochs = epochs + 1
totalRewardsInEpochs.append(totalReward)
totalReward = 0
averageQValuesInEpochs.append(
np.round(totalQValue / numQValues, decimals=2))
totalQValue = 0
numQValues = 0
start(trainerBinding)
#trainerBinding.initializeGame("ASDFASDF",startAt=Trainer.START_AT_MONSTER_ROOM)
# train
nextObservation = surfarray.array3d(frameData.surface)
nextObservation = preprocess(nextObservation)
brain.setPerception(nextObservation, action, reward, terminal)
# pull out some frame information
screen.blit(frameData.surface, (0, 0))
display.flip()
clock.tick(60)
quit()