def __init__(self, cost, x0, dt):
super(Car, self).__init__(self.ode, cost, x0, 2, dt)
self.xIsAngle = [False, False, True, False]
self.o = observation(self.x, self.xIsAngle)
self.o_ = self.o
self.oDim = len(self.o) # observation dimensions
self.uMax = np.array([.5, 2.])
def __init__(self, cost, x0, dt):
super(MarBot, self).__init__(self.ode, cost, x0, 1, dt)
self.xIsAngle = [False, True, False, False]
self.o = observation(self.x, self.xIsAngle)
self.oDim = len(self.o) # observation dimensions
self.o_ = self.o
self.uMax = 2 * np.ones(1)
def ode(self, x, u):
self.eval()
o = observation(x, self.xIsAngle)
o = torch.Tensor(o).reshape(1, self.oDim)
o = (o - self.oMean) / self.oVar
u = torch.Tensor(u).reshape(1, self.uDim)
u = (u - self.uMean) / self.uVar
dxdt = self.forward(o, u).detach()
dxdt = (dxdt * self.dxVar + self.dxMean).numpy()
return dxdt[0]
def __init__(self, cost, x0, dt, linearized=True):
self.ode, self.A, self.B = acrobot_ode(linearized=linearized)
super(Acrobot, self).__init__(self.ode, cost, x0, 1, dt)
self.xIsAngle = [True, True, False, False]
self.o = observation(self.x, self.xIsAngle)
self.oDim = len(self.o) # observation dimensions
self.o_ = self.o
if linearized:
self.uMax = 50 * np.ones(1)
else:
self.uMax = 5 * np.ones(1)
def __init__(self, cost, x0, dt, linearized=True):
self.ode, self.A, self.B = cart_pole_triple_ode(linearized=linearized)
super(CartPoleTriple, self).__init__(self.ode, cost, x0, 1, dt)
self.xIsAngle = [False, True, True, True, False, False, False, False]
self.o = observation(self.x, self.xIsAngle)
self.oDim = len(self.o) #observation dimensions
self.o_ = self.o
if linearized:
self.uMax = 20 * np.ones(1)
else:
self.uMax = 100 * np.ones(1)
self.x1Max = 1.5
def __init__(self, cost, x0, dt, linearized=True, task='swing_up'):
self.ode, self.A, self.B = cart_pole_double_serial_ode(
linearized=linearized)
super(CartPoleDoubleSerial, self).__init__(self.ode, cost, x0, 1, dt)
self.xIsAngle = [False, True, True, False, False, False]
self.o = observation(self.x, self.xIsAngle)
self.oDim = len(self.o) #observation dimensions
self.o_ = self.o
if linearized:
self.uMax = 15 * np.ones(1)
else:
self.uMax = 100 * np.ones(1)
self.task = task
self.x1Max = 1.5
def observe(self, x):
obsv = observation(x, self.xIsAngle)
return obsv