def step(self, obs):
super(MoveToBeacon, self).step(obs)
if MoveToBeacon.loaded == False:
self.loadK("8Dense-10-epochs-2-batches-3000-dataSetSize-98%")
MoveToBeacon.loaded = True
input = obs.observation.feature_minimap[5]
for x in input:
output = ""
for i in x:
output+=str(i)
output+=""
print(output)
print("\n")
#If maring is selected, use DNN
#print("Single: " + (str)(len(obs.observation.single_select)))
#print("Multi: " + (str)(len(obs.observation.multi_select)))
if self.unit_type_is_selected(obs, units.Terran.Marine):
if self.can_do(obs, actions.FUNCTIONS.Attack_screen.id):
newInput = self.stencil(obs.observation.feature_minimap[3], obs.observation.feature_minimap[5])
#for x in stencil:
# output = ""
# for i in x:
# output+=str(i)
# output+=""
# print(output)
#print("\n")
#for x in input:
# output = ""
# for i in x:
# output+=str(i)
# output+=""
# print(output)
#print("\n")
#for x in newInput:
# output = ""
# for i in x:
# output+=str(i)
# output+=" "
# print(output)
#print("\n")
newInput = numpy.expand_dims(newInput, axis=2)
newInput = newInput.reshape([-1,const.InputSize(),const.InputSize(),1])
prediction = self.model.predict(newInput)
outputx = prediction[0][0] * const.ScreenSize().x
outputy = prediction[0][1] * const.ScreenSize().y
#print(('Network Prediction vs Optimum: ({},{}) ({},{})'.format(int(outputx),int(outputy),beacon.x,beacon.y)), end='\r')
return actions.FUNCTIONS.Attack_screen("now", (outputx,outputy))
#Select Marine
else:
marine = self.get_units_by_type(obs, units.Terran.Marine)
if len(marine) > 0:
return actions.FUNCTIONS.select_point('select_all_type', (marine[0].x,
marine[0].y))
#features.MinimapFeatures.
return actions.FUNCTIONS.no_op()
def get_training_data(training_data_dir):
all_files = os.listdir(training_data_dir)
all_files_size = len([num for num in all_files])
inputs = []
outputs = []
counter = 0
print('Extracting files...')
for file in all_files:
print("{}/{}".format(counter + 1, all_files_size), end='\r')
counter += 1
full_path = os.path.join(training_data_dir, file)
# Extract file code
with open(full_path) as csv_file:
reader = csv.reader(csv_file)
count = 0
inputrows = []
for row in reader:
if count == const.InputSize():
outputs.append(row)
else:
inputrows.append(row)
count += 1
inputs.append(inputrows)
print("{}/{}".format(counter, all_files_size))
inputs = np.expand_dims(inputs, axis=3)
inputs = np.reshape(inputs, (-1, const.InputSize(), const.InputSize(), 1))
outputs = np.reshape(outputs, (-1, const.OutputSize()))
inputs = inputs.astype(np.int)
outputs = outputs.astype(np.float)
return [inputs, outputs]
def stencil(self, _stencil, _raw_list):
input = _raw_list
stencil = _stencil
newInput = numpy.zeros((const.InputSize(),const.InputSize()),int)
counterx = 0
countery = 0
for numy, y in enumerate(stencil):
for numx, x in enumerate(y):
if (x != 0):
newInput[countery][counterx] = input[numy][numx]
counterx+=1
if (counterx == const.InputSize()):
countery+=1
counterx=0
return newInput
def getSimplifiedInput(self, obs):
#31x31 Simplified
input = obs.observation.feature_minimap[5]
stencil = obs.observation.feature_minimap[3]
newInput = numpy.zeros((const.InputSize(),const.InputSize()),int)
counterx = 0
countery = 0
for numy, y in enumerate(stencil):
for numx, x in enumerate(y):
if (x == 1):
newInput[countery][counterx] = input[numy][numx]
counterx+=1
if (counterx == const.InputSize()):
countery+=1
counterx=0
return newInput
class GenerateMoveToBeaconTestData(base_agent.BaseAgent):
#Pysc2 defs
packagedInput = numpy.zeros((const.InputSize(),const.InputSize()),int)
packagedOutput = numpy.empty(const.OutputSize(), float)
packageCounter = 0
def get_obs(self, obs):
return {self.screen: obs['screen'],
self.available_actions: obs['available_actions']}
def get_units_by_type(self, obs, unit_type):
return [unit for unit in obs.observation.feature_units
if unit.unit_type == unit_type]
def can_do(self, obs, action):
return action in obs.observation.available_actions
def unit_type_is_selected(self, obs, unit_type):
if (len(obs.observation.single_select) > 0 and
obs.observation.single_select[0].unit_type == unit_type):
return True
def step(self, obs):
super(GenerateMoveToBeaconTestData, self).step(obs)
# Extract file code
# with open ('training_data/0.csv') as csv_file:
# reader = csv.reader(csv_file)
# count = 0
# for row in reader:
# if count == 0:
# GenerateMoveToBeaconTestData.packagedInput = row
# if count == 2:
# GenerateMoveToBeaconTestData.packagedOutput = row
# count+=1
#If previous action was successful, record as training data
if obs.reward > 0:
#counter = 0
#output = ""
#for x in GenerateMoveToBeaconTestData.packagedInput:
# output+=str(x)
# output+=" "
# counter+=1
# if (counter == const.InputSize()):
# print(output)
# output = ""
# counter = 0
#print(GenerateMoveToBeaconTestData.packagedOutput)
fileName = 'raw_training_data/' + str(self.packageCounter) + '.csv'
with open(fileName, mode='w', newline='') as file:
writer = csv.writer(file)
writer.writerows(self.packagedInput)
writer.writerow(self.packagedOutput)
self.packageCounter+=1
#if (self.packageCounter == 3000):
# self.packageCounter = 4000
newinput = obs.observation.feature_screen[6]
#stencil = obs.observation.feature_minimap[3]
##24x24 is refined input data size
##Use camera stencil to grab relevent data
#newinput = numpy.zeros((const.InputSize(),const.InputSize()),int)
#counterx = 0
#countery = 0
#for numy, y in enumerate(stencil):
# for numx, x in enumerate(y):
# if (x == 1):
# newinput[countery][counterx] = input[numy][numx]
# counterx+=1
# if (counterx == const.InputSize()):
# countery+=1
# counterx=0
for unit in obs.observation.feature_units:
if(unit.unit_type == 317):
beacon = unit
#for x in newinput:
# output = ""
# for i in x:
# output+=str(i)
# output+=""
# print(output)
#print("\n")
#Screen is not 84x84 but ~84x60 but 84x84 for simplicity
outputx = beacon.x #random.randint(0,const.ScreenSize())
outputy = beacon.y #random.randint(0,const.ScreenSize())
self.packagedInput = newinput
#/84 to get a number between 0 and 1 as outputs for DNN
self.packagedOutput = [float(round(Decimal(outputx/const.ScreenSize().x),2)),
float(round(Decimal(outputy/const.ScreenSize().y),2))]
if self.unit_type_is_selected(obs, units.Terran.Marine):
if self.can_do(obs, actions.FUNCTIONS.Attack_screen.id):
return actions.FUNCTIONS.Attack_screen("now", (outputx,outputy))
#Select Marine
else:
marine = self.get_units_by_type(obs, units.Terran.Marine)
if len(marine) > 0:
return actions.FUNCTIONS.select_point('select_all_type', (marine[0].x,
marine[0].y))
#features.MinimapFeatures.
return actions.FUNCTIONS.no_op()
def step(self, obs):
super(DefeatEnemies, self).step(obs)
if DefeatEnemies.loaded == False:
self.loadK("LSTM84")
DefeatEnemies.loaded = True
if self.unit_type_is_selected(obs, units.Terran.Marine):
if self.can_do(obs, actions.FUNCTIONS.Attack_screen.id):
#84x84 Detailed
#Things to be known:
#Height_map(0) visibility(1) creep(2) Player_relative(5) Unit_type(6) selected(7) hit_points(8) unit_density(11)
input = obs.observation.feature_screen[6]
#features.Dimensions.screen
#31x31 Simplified
#input = obs.observation.feature_screen[5]
#stencil = obs.observation.feature_screen[3]
#newInput = numpy.zeros((const.InputSize(),const.InputSize()),int)
#counterx = 0
#countery = 0
#for numy, y in enumerate(stencil):
# for numx, x in enumerate(y):
# if (x == 1):
# newInput[countery][counterx] = input[numy][numx]
# counterx+=1
# if (counterx == const.InputSize()):
# countery+=1
# counterx=0
obs.observation
for x in obs.observation.feature_screen[6]:
output = ""
for i in x:
output+=str(i)
output+=""
print(output)
print("\n")
newInput = numpy.expand_dims(input, axis=2)
newInput = newInput.reshape([-1,const.InputSize(),const.InputSize(),1])
#self.model.fit(TD[0], TD[1],
#batch_size=batch_size,
#epochs=epochs,
#validation_split=0.1,
#shuffle=False, verbose=verbose)
prediction = self.model.predict(newInput)
#for marines in obs.observation.multi_select:
# obs.observation.multi_select[0]
# prediction = self.model.predict(newInput)
outputx = prediction[0][0] * const.ScreenSize().x
outputy = prediction[0][1] * const.ScreenSize().y
#return actions.FunctionCall(function_id, args)
return actions.FUNCTIONS.Attack_screen("now", (outputx,outputy))
else:
marine = self.get_units_by_type(obs, units.Terran.Marine)
if len(marine) > 0:
return actions.FUNCTIONS.select_point('select_all_type', (marine[0].x,
marine[0].y))