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float | double_cartpole_learn.dt = 0.05 |
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dictionary | double_cartpole_learn.model_parameters = {} |
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list | double_cartpole_learn.x0 = [0,0,0,0,np.pi,np.pi] |
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tuple | double_cartpole_learn.S0 = np.eye(6) |
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tuple | double_cartpole_learn.measurement_noise = np.diag(np.ones(len(x0))*0.01**2) |
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tuple | double_cartpole_learn.plant = DoubleCartpole(model_parameters,x0,S0,dt,measurement_noise) |
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tuple | double_cartpole_learn.draw_dcp = DoubleCartpoleDraw(plant,0.033) |
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list | double_cartpole_learn.angle_dims = [4,5] |
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tuple | double_cartpole_learn.policy = RBFPolicy(x0,S0,[20],200, angle_dims) |
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dictionary | double_cartpole_learn.cost_parameters = {} |
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tuple | double_cartpole_learn.cost = partial(double_cartpole_loss, params=cost_parameters) |
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float | double_cartpole_learn.T = 5.0 |
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int | double_cartpole_learn.J = 2 |
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int | double_cartpole_learn.N = 40 |
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tuple | double_cartpole_learn.learner = PILCO(plant, policy, cost, angle_dims, async_plant=False) |
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