class MountainCarEnv(Env):
print_interval = 100
def __init__(self):
self.env = MountainCar()
self.noiseRange = 1.0
self.om = 0
self.alpha = 0.6
self.beta = 0.4
self.t = 0
self.totStep = 0
self.r = 0
self.ep = 0
self.perfs = result_log(algo="DDPG", l1=20, l2=10)
self.actif = True
#self.plot = result_plot()
def state(self):
return [self.env.getObservation()]
def act(self, action):
actNoise = action + self.noise_func()
self.env.performAction(actNoise[0])
r = self.env.getReward()
self.t += 1
self.r += r
return actNoise, [r]
def reset(self, noise=True):
self.actif = True
self.env.reset()
self.om = 0
self.totStep+=self.t
if self.totStep != 0:
self.perfs.addData(self.totStep, self.t, self.r)
self.t = 0
self.r = 0
self.ep += 1
if not noise:
self.noiseRange = 0.0
else:
self.noiseRange = random.uniform(0.,1.0)
def noise_func(self):
self.om = self.om-self.alpha*self.om + self.beta*random.gauss(0,1)*self.noiseRange
return self.om
def isFinished(self):
if self.actif and not self.env.isFinished():
return False
else:
self.actif = False
return True
def getActionSize(self):
return 1
def getStateSize(self):
return 2
def getActionBounds(self):
return [[1.2], [-1.2]]
def printEpisode(self):
print time.strftime("[%H:%M:%S]"), " Episode : " , self.ep, " steps : ", self.t, " reward : ", self.r
def performances(self):
pass#self.plot.clear()
#self.plot.add_row(self.perfs)
