41 lines
1.5 KiB
Plaintext
41 lines
1.5 KiB
Plaintext
Random Seed: 4529
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Time Span: 0 to 10, Points: 1000
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Learning Rate: 0.01
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Weight Decay: 0.0001
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Loss Function:
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def loss_fn(state_traj, t_span):
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theta = state_traj[:, :, 0]
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desired_theta = state_traj[:, :, 3]
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# Make the time weights broadcastable for theta [len(t_span), len(batches)]
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lamda_exp = 1
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time_weights = torch.exp(lamda_exp * t_span).view(-1, 1)
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return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2)
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Training Cases:
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[theta0, omega0, alpha0, desired_theta]
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[0.5235987901687622, 0.0, 0.0, 0.0]
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[-0.5235987901687622, 0.0, 0.0, 0.0]
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[2.094395160675049, 0.0, 0.0, 0.0]
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[-2.094395160675049, 0.0, 0.0, 0.0]
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[0.0, 1.0471975803375244, 0.0, 0.0]
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[0.0, -1.0471975803375244, 0.0, 0.0]
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[0.0, 6.2831854820251465, 0.0, 0.0]
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[0.0, -6.2831854820251465, 0.0, 0.0]
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[0.0, 0.0, 0.0, 6.2831854820251465]
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[0.0, 0.0, 0.0, -6.2831854820251465]
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[0.0, 0.0, 0.0, 1.5707963705062866]
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[0.0, 0.0, 0.0, -1.5707963705062866]
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[0.0, 0.0, 0.0, 1.0471975803375244]
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[0.0, 0.0, 0.0, -1.0471975803375244]
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[0.7853981852531433, 3.1415927410125732, 0.0, 0.0]
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[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0]
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[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244]
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[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244]
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[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465]
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[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465]
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[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293]
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[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293]
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