def punishLastAction(self, howmuch):
super().punishLastAction(howmuch)
if self.containers.showscreen:
infoscreen.print(str(-abs(howmuch)),
time.strftime("%H:%M:%S", time.gmtime()),
containers=self.containers,
wname="Last big punish")
def learnANN(self):
tmp = super().learnANN()
print("ReinfLearnSteps:", self.model.step(), level=3)
if self.containers.showscreen:
infoscreen.print(self.model.step(),
"Iterations: >" +
str(self.model.run_inferences()),
containers=self.containers,
wname="ReinfLearnSteps")
return tmp
def policyAction(self, agentState):
action, qvals = self.model.inference(self.makeInferenceUsable(agentState)) #former is argmax, latter are individual qvals
throttle, brake, steer = self.dediscretize(action[0])
toUse = "["+str(throttle)+", "+str(brake)+", "+str(steer)+"]"
self.showqvals(qvals[0])
if self.containers.showscreen:
infoscreen.print(toUse, containers=self.containers, wname="Last command")
if self.model.run_inferences() % 100 == 0:
infoscreen.print(self.model.step(), "Iterations: >"+str(self.model.run_inferences()), containers=self.containers, wname="ReinfLearnSteps")
return toUse, (throttle, brake, steer) #er returned immer toUse, toSave
def showqvals(self, qvals):
amount = self.conf.steering_steps*4 if self.conf.INCLUDE_ACCPLUSBREAK else self.conf.steering_steps*3
b = []
for i in range(amount):
a = [0]*amount
a[i] = 1
b.append(str(self.dediscretize(a)))
b = list(zip(b, qvals))
toprint = [str(i[0])[1:-1]+": "+str(i[1]) for i in b]
toprint = "\n".join(toprint)
print(b, level=3)
if self.containers.showscreen:
infoscreen.print(toprint, containers= self.containers, wname="Current Q Vals")
def addToMemory(self, gameState, pastState):
a, r, qval, count, changestring = super().addToMemory(
gameState, pastState)
if self.containers.showscreen:
infoscreen.print(a,
round(r, 2),
round(qval, 2),
changestring,
containers=self.containers,
wname="Last memory")
if len(self.memory) % 20 == 0:
infoscreen.print(">" + str(len(self.memory)),
containers=self.containers,
wname="Memorysize")
def policyAction(self, agentState):
action, _ = self.model.inference(self.makeInferenceUsable(agentState))
action = self.make_noisy(action[0])
action = [round(i, 3) for i in action]
toUse = "[" + str(action[0]) + ", " + str(action[1]) + ", " + str(
action[2]) + "]"
if self.containers.showscreen:
infoscreen.print(toUse,
containers=self.containers,
wname="Last command")
if self.model.run_inferences() % 100 == 0:
infoscreen.print(self.model.step(),
"Iterations: >" +
str(self.model.run_inferences()),
containers=self.containers,
wname="ReinfLearnSteps")
infoscreen.print(self.epsilon,
containers=self.containers,
wname="Epsilon")
return toUse, action
def eval_episodeVals(self, mem_epi_slice, gameState, endReason):
string = super().eval_episodeVals(mem_epi_slice, gameState, endReason)
if self.containers.showscreen:
infoscreen.print(string, containers=self.containers, wname="Last Epsd")
def randomAction(self, agentState):
toUse, toSave = super().randomAction(agentState)
if self.containers.showscreen:
infoscreen.print(toUse, "(random)", containers=self.containers, wname="Last command")
infoscreen.print(self.epsilon, containers=self.containers, wname="Epsilon")
return toUse, toSave
