Variables
float	dt = 0.1

dictionary	model_parameters = {}

list	x0 = [0,0,0,0]

tuple	S0 = np.eye(4)

list	maxU = [10]

tuple	measurement_noise = np.diag(np.ones(len(x0))0.01*2)

tuple	plant = Cartpole(model_parameters,x0,S0,dt,measurement_noise)

tuple	draw_cp = CartpoleDraw(plant,0.033)

list	angle_dims = [3]

tuple	policy = RBFPolicy(x0,S0,maxU,10, angle_dims)

dictionary	cost_parameters = {}

tuple	cost = partial(cartpole_loss, params=cost_parameters)

float	T = 4.0

int	J = 2

int	N = 100

tuple	learner = PILCO(plant, policy, cost, angle_dims, async_plant=False)

Function Documentation

def kusanagi.examples.cartpole.cartpole_learn.signal_handler	(	signal,
		frame
	)

Variable Documentation

list kusanagi.examples.cartpole.cartpole_learn.angle_dims = [3]

tuple kusanagi.examples.cartpole.cartpole_learn.cost = partial(cartpole_loss, params=cost_parameters)

dictionary kusanagi.examples.cartpole.cartpole_learn.cost_parameters = {}

tuple kusanagi.examples.cartpole.cartpole_learn.draw_cp = CartpoleDraw(plant,0.033)

float kusanagi.examples.cartpole.cartpole_learn.dt = 0.1

int kusanagi.examples.cartpole.cartpole_learn.J = 2

tuple kusanagi.examples.cartpole.cartpole_learn.learner = PILCO(plant, policy, cost, angle_dims, async_plant=False)

list kusanagi.examples.cartpole.cartpole_learn.maxU = [10]

tuple kusanagi.examples.cartpole.cartpole_learn.measurement_noise = np.diag(np.ones(len(x0))*0.01**2)

dictionary kusanagi.examples.cartpole.cartpole_learn.model_parameters = {}

int kusanagi.examples.cartpole.cartpole_learn.N = 100

tuple kusanagi.examples.cartpole.cartpole_learn.plant = Cartpole(model_parameters,x0,S0,dt,measurement_noise)

tuple kusanagi.examples.cartpole.cartpole_learn.policy = RBFPolicy(x0,S0,maxU,10, angle_dims)

tuple kusanagi.examples.cartpole.cartpole_learn.S0 = np.eye(4)

float kusanagi.examples.cartpole.cartpole_learn.T = 4.0

list kusanagi.examples.cartpole.cartpole_learn.x0 = [0,0,0,0]