class DoublePoleEnvironment(Environment):
""" two poles to be balanced from the same cart. """
indim = 1
ooutdim = 6
def __init__(self):
self.p1 = CartPoleEnvironment()
self.p2 = CartPoleEnvironment()
self.p2.l = 0.05
self.p2.mp = 0.01
self.reset()
def getSensors(self):
""" returns the state one step (dt) ahead in the future. stores the state in
self.sensors because it is needed for the next calculation. The sensor return
vector has 6 elements: theta1, theta1', theta2, theta2', s, s'
(s being the distance from the origin).
"""
s1 = self.p1.getSensors()
s2 = self.p2.getSensors()
self.sensors = (s1[0], s1[1], s2[0], s2[1], s2[2], s2[3])
return self.sensors
def reset(self):
""" re-initializes the environment, setting the cart back in a random position.
"""
self.p1.reset()
self.p2.reset()
# put cart in the same place:
self.p2.sensors = (self.p2.sensors[0], self.p2.sensors[1],
self.p1.sensors[2], self.p1.sensors[3])
self.getSensors()
def performAction(self, action):
""" stores the desired action for the next runge-kutta step.
"""
self.p1.performAction(action)
self.p2.performAction(action)
def getCartPosition(self):
""" auxiliary access to just the cart position, to be used by BalanceTask """
return self.sensors[4]
def getPoleAngles(self):
""" auxiliary access to just the pole angle(s), to be used by BalanceTask """
return [self.sensors[0], self.sensors[2]]
class DoublePoleEnvironment(Environment):
""" two poles to be balanced from the same cart. """
indim = 1
ooutdim = 6
def __init__(self):
self.p1 = CartPoleEnvironment()
self.p2 = CartPoleEnvironment()
self.p2.l = 0.05
self.p2.mp = 0.01
self.reset()
def getSensors(self):
""" returns the state one step (dt) ahead in the future. stores the state in
self.sensors because it is needed for the next calculation. The sensor return
vector has 6 elements: theta1, theta1', theta2, theta2', s, s'
(s being the distance from the origin).
"""
s1 = self.p1.getSensors()
s2 = self.p2.getSensors()
self.sensors = (s1[0], s1[1], s2[0], s2[1], s2[2], s2[3])
return self.sensors
def reset(self):
""" re-initializes the environment, setting the cart back in a random position.
"""
self.p1.reset()
self.p2.reset()
# put cart in the same place:
self.p2.sensors = (self.p2.sensors[0], self.p2.sensors[1], self.p1.sensors[2], self.p1.sensors[3])
self.getSensors()
def performAction(self, action):
""" stores the desired action for the next runge-kutta step.
"""
self.p1.performAction(action)
self.p2.performAction(action)
def getCartPosition(self):
""" auxiliary access to just the cart position, to be used by BalanceTask """
return self.sensors[4]
def getPoleAngles(self):
""" auxiliary access to just the pole angle(s), to be used by BalanceTask """
return [self.sensors[0], self.sensors[2]]
def getSensors(self):
""" returns the state one step (dt) ahead in the future. stores the state in
self.sensors because it is needed for the next calculation. The sensor return
vector has 2 elements: theta, s
(s being the distance from the origin).
"""
tmp = CartPoleEnvironment.getSensors(self)
return (tmp[0], tmp[2])
def getSensors(self):
""" returns the state one step (dt) ahead in the future. stores the state in
self.sensors because it is needed for the next calculation. The sensor return
vector has 2 elements: theta, s
(s being the distance from the origin).
"""
tmp = CartPoleEnvironment.getSensors(self)
return (tmp[0], tmp[2])
def __init__(self, env=None, maxsteps=1000):
"""
:key env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
:key maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = CartPoleEnvironment()
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
# no scaling of sensors
self.sensor_limits = [None] * 2
# scale actor
self.actor_limits = [(-50, 50)]
def __init__(self, env=None, maxsteps=1000):
"""
@param env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
@param maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = CartPoleEnvironment()
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
# scale position and angle, don't scale velocities (unknown maximum)
self.sensor_limits = [(-3, 3)] #, None, (-pi, pi), None]
for i in range(1, self.outdim):
if isinstance(self.env, NonMarkovPoleEnvironment) and i % 2 == 0:
self.sensor_limits.append(None)
else:
self.sensor_limits.append((-pi, pi))
self.sensor_limits = [None] * 4
# actor between -10 and 10 Newton
self.actor_limits = [(-10, 10)]
def __init__(self):
self.p1 = CartPoleEnvironment()
self.p2 = CartPoleEnvironment()
self.p2.l = 0.05
self.p2.mp = 0.01
self.reset()
def __init__(self):
self.p1 = CartPoleEnvironment()
self.p2 = CartPoleEnvironment()
self.p2.l = 0.05
self.p2.mp = 0.01
self.reset()
