class ReplayBuffer():
def __init__(self, agent_num, model_structure, model_weight, height, width, path = "./buffer", cuda=False):
#initial the model in the replay buffer
#model is be defined using pytorch lib
self.model = model_structure
self.model_weight = model_weight
self.loadWeight()
#the game is the enviroment of route planning game in the project
self.height = height
self.width = width
self.game = None
self.hx = None
self.cx = None
self.bot_observe = None
# self.agent_numbers = int(((self.height + self.width) / 2) * 0.3)
self.agent_numbers = agent_num
#default score setting in the game
self.acquisition_sum = 700
self.explored_target_sum = 70
self.explored_sum = 40
self.time_decrease = -0.00005
self.crash_time_penalty = -0.0001
self.crash_sum = -20
#path is the folder of the replay buffer
self.path = path
if not os.path.exists(path):
os.makedirs(path)
self.IOlimit = 20
self.buffer_limit = 1000
self.cuda = cuda
print(cuda)
def collect(self, game_round):
#data is stored in the python list using pickle
#data list is a 2d list index is the batch index of collection
if self.game == None:
self.initialGame()
if self.cuda:
self.model.cuda()
#collecting part
while True:
data = []
collect_iter = 0
while collect_iter < self.IOlimit:
temp = []
if self.game.active and self.game.state < game_round:
#observe part of the bot
# bot_observe = np.zeros((self.agent_numbers, 3, self.width, self.height))
# for i in range(self.agent_numbers):
# bot_observe[i] = self.game.outputAgentImage(i)
if self.bot_observe is None:
self.bot_observe = [self.game.outputAgentImage(i) for i in range(self.agent_numbers)]
self.bot_observe = np.array(bot_observe).astype(np.uint8)
# bot_observe = np.uint8(bot_observe)
#batch * width * height * 3 (numpy array)[0]
temp.append(self.bot_observe)
temp.append(self.hx) #batch * 512[1](tensor)[1]
temp.append(self.cx) #batch * 512[1](tensor)[2]
#model output process
self.bot_observe = torch.from_numpy(self.bot_observe).float()
if self.cuda:
self.hx = self.hx.cuda()
self.cx = self.cx.cuda()
self.bot_observe = self.bot_observe.cuda()
critic_score, bot_command, (self.hx, self.cx) = self.model(self.bot_observe, self.hx, self.cx)
#bot action tensor
temp.append(bot_command.cpu()) #tensor[3]
#game score output
temp.append(self.game.outputScore()) #float[4]
#input the commands of the model to the game
commands = self.interpretAction(bot_command.cpu())
self.game.runOneRound(commands)
self.bot_observe = [self.game.outputAgentImage(i) for i in range(self.agent_numbers)]
self.bot_observe = np.array(self.bot_observe).astype(np.uint8)
#batch * width * height * 3 (numpy array)[5]
temp.append(self.bot_observe)
temp.append(self.hx.cpu()) #batch * 512[1](tensor)[6]
temp.append(self.cx.cpu()) #batch * 512[1](tensor)[7]
#game score output
temp.append(self.game.outputScore()) #float[8]
data.append(temp)
collect_iter += 1
if collect_iter % 5 == 0 and collect_iter is not 0:
print('Collecting Progress: ', collect_iter, ' / ', self.IOlimit, ' | Score: %02.5f' % self.game.outputScore())
else:
#only occur when the game is not active
self.initialGame()
#print('Collecting process done\nStart writing file...')
for i in range(self.IOlimit):
filename = self.path + '/' + datetime.datetime.now().isoformat() + '.pkl'
save_object(filename, data[i])
#print('All collection process done\n')
del data
self.loadWeight()
def loadWeight(self):
if os.path.exists(self.model_weight):
self.model.load_state_dict(torch.load(self.model_weight))
else:
print('Model weight [{0}] not found'.format(self.model_weight))
return
def initialGame(self):
#initial the game envirnment for th replay buffer
t = (self.height + self.width) / 2
self.game = Game(self.height, self.width)
self.game.setRandomMap(self.agent_numbers, int(t * 0.3) ** 2, 1)
self.game.setScore(self.acquisition_sum, self.explored_target_sum, self.explored_sum, self.time_decrease, self.crash_time_penalty, self.crash_sum)
commands = [Command(i, 0, 0) for i in range(self.agent_numbers)]
self.game.runOneRound(commands)
#the memory tensor of the model in the lstm
self.hx = torch.zeros([self.agent_numbers, 512], dtype=torch.float32)
self.cx = torch.zeros([self.agent_numbers, 512], dtype=torch.float32)
self.bot_observe = [self.game.outputAgentImage(i) for i in range(self.agent_numbers)]
self.bot_observe = np.array(self.bot_observe).astype(np.uint8)
print('New Game!')
def resetGameScore(self, acquisition_sum, explored_target_sum, explored_sum, time_decrease, crash_time_penalty, crash_sum):
#the score calculating standard of the game
self.acquisition_sum = acquisition_sum
self.explored_target_sum = explored_target_sum
self.explored_sum = explored_sum
self.time_decrease = time_decrease
self.crash_time_penalty = crash_time_penalty
self.crash_sum = crash_sum
def countFileNum(self, path):
file_list = os.listdir(path)
count = 0
for i in range(len(file_list)):
if file_list[i].endswith('.pkl'):
count += 1
return count
def grabFileName(self, path):
file_list = os.listdir(path)
name_list = []
for i in range(len(file_list)):
if file_list[i].endswith('.pkl'):
name_list.append(file_list[i])
return name_list
def resetIOBatch(self, new_limit):
self.IOlimit = new_limit
def resetBufferLimit(self, new_limit):
self.buffer_limit = new_limit
def interpretAction(self, command_tensor):
command_tensor = command_tensor.view(-1, 5)
command = torch.max(command_tensor, 1)[1].tolist()
commands = [self.intoCommand(i, command[i]) for i in range(len(command))]
return commands
def intoCommand(self, i, command):
if command == 0: return Command(i, 0, 1)
elif command == 1: return Command(i, 1, 0)
elif command == 2: return Command(i, 0, -1)
elif command == 3: return Command(i, -1, 0)
return Command(i, 0, 0)
class ReplayBuffer():
def __init__(self, agent_num, height, width, model, modelpath, game_round, cuda=True):
#initial the model in the replay buffer
#model is be defined using pytorch lib
#the game is the enviroment of route planning game in the project
self.height = height
self.width = width
self.game_round = game_round
self.agent_num = agent_num
self.model = model
self.modelpath = modelpath
self.cuda = cuda
self.state = torch.zeros([self.agent_num, 3, self.height, self.width], dtype=torch.float32)
#default score setting in the game
self.acquisition_sum = 400
self.explored_target_sum = 70
self.explored_sum = 40
self.total_score = self.acquisition_sum + self.explored_target_sum + self.explored_sum
self.time_decrease = -0.00005
self.crash_time_penalty = -0.0001
self.crash_sum = -400
self.reg_val = 1
# self.action = torch.tensor([random.randint(0, 4) for i in range(self.agent_num)])
self.memory = []
self.buffer_limit = 1000
self.game = None
self.score = None
self.initialGame()
def collect(self, model, verbose=1):
if self.cuda:
self.model.cuda()
self.model.load_state_dict(model.state_dict())
if self.game.active and self.game.state < self.game_round:
#observe part of the bot
action = self.select_action()
self.game.runOneRound([intoCommand(i, action[i]) for i in range(self.agent_num)])
next_state = np.array([self.game.outputAgentImage(i) for i in range(self.agent_num)]).astype(np.float32)
next_state = torch.from_numpy(next_state)
if verbose == 1:
print(self.game.outputScore())
done = False
if self.game.active is False:
done = True
data = Data(self.state, action, next_state)
self.memory.append(data)
self.state = next_state
if len(self.memory) > self.buffer_limit:
self.memory.remove(self.memory[0])
else:
#only occur when the game is not active
self.initialGame()
def select_action(self):
return torch.tensor([random.randint(0, 4) for i in range(self.agent_num)])
def save_state(self, path):
for i in range(self.agent_num):
img = np.zeros([32, 32, 3], dtype=np.uint8)
arr = self.game.outputAgentImage(i)
img[:,:,0] = arr[0,:,:]
img[:,:,1] = arr[1,:,:]
img[:,:,2] = arr[2,:,:]
plt.subplot(2,2,i + 1)
plt.imshow(img)
plt.show()
def play(self, model, game_round):
while self.game.active and self.game.state < game_round:
self.states[0] = self.states[1]
self.states[1] = self.states[2]
self.states[2] = self.states[3]
s = np.array([self.game.outputAgentImage(i) for i in range(self.agent_num)]).astype(np.float32)
s = torch.from_numpy(s)
self.states[3] = s
action = model(self.states[1:].unsqueeze(0)).max(2)[1].view(self.agent_num)
print(action)
action = [intoCommand(i, action[i]) for i in range(self.agent_num)]
self.game.runOneRound(action)
print(self.game.outputScore())
sleep(0.1)
def initialGame(self):
#initial the game envirnment for th replay buffer
t = (self.height + self.width) / 2
self.game = Game(self.height, self.width, self.game_round)
self.game.setRandomMap(self.agent_num, int(t * 0.3) ** 2, int(t * 0.1) ** 2)
self.game.setScore(self.acquisition_sum, self.explored_target_sum, self.explored_sum, self.time_decrease, self.crash_time_penalty, self.crash_sum, self.reg_val)
self.game.runOneRound([Command(i, 0, 0) for i in range(self.agent_num)])
self.score = np.array([self.game.outputScore() for i in range(self.agent_num)])
self.state = np.array([self.game.outputAgentImage(i) for i in range(self.agent_num)]).astype(np.float32)
self.state = torch.from_numpy(self.state)
print('New Game!')
def loadWeight(self):
if os.path.exists(self.modelpath):
if self.cuda:
self.model.load_state_dict(torch.load(self.modelpath))
else:
self.model.load_state_dict(torch.load(self.modelpath, map_location='cpu'))
else:
print('Model weight [{0}] not found'.format(self.modelpath))
return