64 lines
2.7 KiB
Plaintext
64 lines
2.7 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 Name: five
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Loss Function Exponent: 5
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Current Loss Function (wrapper) Source Code:
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def current_loss_fn(state_traj):
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theta = state_traj[:, :, 0] # [batch_size, t_points]
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desired_theta = state_traj[:, :, 3] # [batch_size, t_points]
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return torch.mean(loss_fn(theta, desired_theta))
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Specific Loss Function Source Code:
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def five_loss(theta: torch.Tensor, desired_theta: torch.Tensor, min_val: float = 0.01) -> torch.Tensor:
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return normalized_loss(theta, desired_theta, exponent=5, min_val=min_val)
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Normalized Loss Function Source Code:
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def normalized_loss(theta: torch.Tensor, desired_theta: torch.Tensor, exponent: float, min_val: float = 0.01, delta: float = 1) -> torch.Tensor:
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"""
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Computes a normalized loss that maps the error (|theta - desired_theta|) on [0, 2π]
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to the range [min_val, 1]. To avoid an infinite gradient at error=0 for exponents < 1,
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a shift 'delta' is added.
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The loss is given by:
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loss = min_val + (1 - min_val) * ( ((error + delta)^exponent - delta^exponent)
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/ ((2π + delta)^exponent - delta^exponent) )
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so that:
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- When error = 0: loss = min_val
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- When error = 2π: loss = 1
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"""
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error = torch.abs(theta - desired_theta)
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numerator = (error + delta) ** exponent - delta ** exponent
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denominator = (2 * math.pi + delta) ** exponent - delta ** exponent
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return min_val + (1 - min_val) * (numerator / denominator)
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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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