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L2 weight decay for Q

See original GitHub issue

The paper mentions “For Q we included L2 weight decay of 0.01 and used a discount factor of gamma = 0.99”

Does that mean that we need to add L2 regularisation to each layer in the critic network?

Maybe something like this in create_critic_network

net = tflearn.add_weights_regularizer(net, 'L2', weight_decay=0.01)

Issue Analytics

State:
Created 6 years ago
Comments:5 (1 by maintainers)

Top GitHub Comments

1reaction

Anjum48commented, Mar 25, 2017

Cool - thanks! I think I’ve got it to work now for my example (non-pendulum). For your code just add this at line 177 in ddpg.py

self.L2 = np.sum([tf.nn.l2_loss(v) for v in self.network_params if "W" in v.name])
self.loss = tflearn.mean_square(self.predicted_q_value, self.out) + L2_DECAY * self.L2

0reactions

AlEmerichcommented, Aug 29, 2018

Hi (Sorry for my bad manners 😃 ) and thank you for the precision !

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