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Speed up the policy optimization by @tf.function #57

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Speed up the policy optimization by @tf.function #57

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1 change: 1 addition & 0 deletions pilco/models/pilco.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,6 +115,7 @@ def optimize_policy(self, maxiter=50, restarts=1):
def compute_action(self, x_m):
return self.controller.compute_action(x_m, tf.zeros([self.state_dim, self.state_dim], float_type))[0]

@tf.function
def predict(self, m_x, s_x, n):
loop_vars = [
tf.constant(0, tf.int32),
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1 change: 1 addition & 0 deletions pilco/rewards.py
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Expand Up @@ -16,6 +16,7 @@ def __init__(self, state_dim, W=None, t=None):
else:
self.t = Parameter(np.zeros((1, state_dim)), trainable=False)

@tf.function
def compute_reward(self, m, s):
'''
Reward function, calculating mean and variance of rewards, given
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