49 lines
2.2 KiB
Plaintext
49 lines
2.2 KiB
Plaintext
Base controller path: /home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth
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Time Span: 0 to 10, Points: 1000
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Learning Rate: 0.1
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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] # Size: [batch_size, t_points]
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desired_theta = state_traj[:, :, 3] # Size: [batch_size, t_points]
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min_weight = 0.01 # Weights are on the range [min_weight, 1]
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weights = weight_fn(t_span, min_val=min_weight) # Initially Size: [t_points]
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# Reshape or expand weights to match theta dimensions
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weights = weights.view(-1, 1) # Now Size: [batch_size, t_points]
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# Calculate the weighted loss
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return torch.mean(weights * (theta - desired_theta) ** 2)
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Weight Function:
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def linear_mirrored(t_span: Union[torch.Tensor, List[float]], t_max: float = None, min_val: float = 0.01) -> torch.Tensor:
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t_span = t_span.clone().detach() if isinstance(t_span, torch.Tensor) else torch.tensor(t_span)
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t_max = t_max if t_max is not None else t_span[-1]
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return min_val + ((1 - min_val) / (t_max)**1) * (-t_span + t_max)**1
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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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