50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
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import numpy as np
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class GaussianPolicy:
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def __init__(self, nr_steps, nr_weights, nr_dims, lowerb=-1.0, upperb=1.0, seed=None):
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self.nr_weights = nr_weights
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self.nr_steps = nr_steps
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self.nr_dims = nr_dims
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self.weights = None
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self.trajectory = None
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self.lowerb = lowerb
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self.upperb = upperb
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self.rng = np.random.default_rng(seed=seed)
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# initialize
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self.mid_points = np.linspace(0, self.nr_steps, self.nr_weights)
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if nr_weights > 1:
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self.std = self.mid_points[1] / (2 * np.sqrt(2 * np.log(2))) # Full width at half maximum
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else:
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self.std = self.nr_steps / 2
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self.reset()
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def reset(self):
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self.weights = np.zeros((self.nr_weights, self.nr_dims))
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self.trajectory = np.zeros((self.nr_steps, self.nr_dims))
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def random_weights(self):
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for dim in range(self.nr_dims):
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self.weights[:, dim] = self.rng.uniform(self.lowerb, self.upperb, self.nr_weights)
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def rollout(self):
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self.trajectory = np.zeros((self.nr_steps, self.nr_dims))
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for step in range(self.nr_steps):
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for weight in range(self.nr_weights):
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base_fun = np.exp(-0.5 * (step - self.mid_points[weight]) ** 2 / self.std ** 2)
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for dim in range(self.nr_dims):
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self.trajectory[step, dim] += base_fun * self.weights[weight, dim]
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return self.trajectory
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def set_weights(self, x):
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self.weights = x.reshape(self.nr_weights, self.nr_dims)
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def get_x(self):
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return self.weights.reshape(self.nr_weights * self.nr_dims, 1)
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