class Environment:
def __init__(self, g=10.0, d=100.0, H=10., m=10.0, F=3.0):
"""Instanciate a new environement in its initial state.
"""
self.mc = MountainCar(g=g, d=d, H=H, m=m, F=F, R=50.0, T=0.0)
def reset(self):
self.mc.reset()
# place the car at a random place near the bottom
self.mc.x = np.random.uniform(-self.mc.d*1.3, -self.mc.d*0.7)
def get_range(self):
return [-1.5*self.mc.d, 0.0]
def observe(self):
"""Returns the current observation that the agent can make
of the environment, if applicable.
"""
return (self.mc.x, self.mc.vx)
def act(self, action):
"""Perform given action by the agent on the environment,
and returns a reward.
"""
reward = self.mc.act(action)
return (reward, "victory" if reward > 0.0 else None)
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)
return X, Y, val.reshape((100,100)).T
render_value_fn = True
file_path = './'
if render_value_fn:
plt.contourf(*getValueFn(valuefn))
plt.title('initial')
plt.savefig(file_path + '0.png')
num_episodes = 500
k = 1
for i in xrange(num_episodes):
r_t, s_t = domain.reset()
agent.reset()
count = 0
cumulative_reward = 0
while s_t != None:
# apply an action from the agent
# the domain will return a 'None' state when terminating
r_t, s_t = domain.step(agent.step(r_t, s_t))
count += 1
cumulative_reward += r_t
# final update step for the agent
agent.step(r_t, s_t)
if i % 2 == 0:
