hiro/ray
1
0
Fork 0
mirror of https://github.com/vale981/ray synced 2025-03-06 10:31:39 -05:00
Code Issues Projects Releases Packages Wiki Activity Actions

[RLlib] Tf-eager policy bug fix: Duplicate model call in compute_gradients. (#12682)

Browse source
This commit is contained in:
Sven Mika 2020-12-09 08:03:58 +01:00 • committed by GitHub
parent cab46b7931
commit 28108c905b
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 0 additions and 8 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

8
rllib/policy/eager_tf_policy.py
View file

@ -594,14 +594,6 @@ def build_eager_tf_policy(name,
self._is_training = True
with tf.GradientTape(persistent=gradients_fn is not None) as tape:
# TODO: set seq len and state-in properly
state_in = []
for i in range(self.num_state_tensors()):
state_in.append(samples["state_in_{}".format(i)])
self._state_in = state_in
model_out, _ = self.model(samples, self._state_in,
samples.get("seq_lens"))
loss = loss_fn(self, self.model, self.dist_class, samples)
variables = self.model.trainable_variables()