wassname/ray
1
0
Fork 0
mirror of https://github.com/wassname/ray.git synced 2026-06-27 19:32:11 +08:00
Code Issues Packages Projects Releases Wiki Activity

Fix TF2 / rllib test (#5846)

Browse Source
This commit is contained in:
Eric Liang
2019-10-07 14:25:16 -07:00
committed by GitHub
parent 9bb3633cd9
commit 04e997fe0d
5 changed files with 12 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ci/jenkins_tests/run_rllib_tests.sh
+6 -6
View File
@@ -340,12 +340,6 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_model.py --run=DQN --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatAfterMeEnv
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatInitialEnv
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=PG --stop=50
@@ -495,3 +489,9 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_ignore_worker_failure.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatAfterMeEnv
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatInitialEnv
rllib/contrib/maddpg/maddpg_policy.py
+3 -3
View File
@@ -11,7 +11,7 @@ from ray.rllib.utils.annotations import override
from ray.rllib.utils.error import UnsupportedSpaceException
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.tf_policy import TFPolicy
from ray.rllib.utils import try_import_tf
from ray.rllib.utils import try_import_tf, try_import_tfp
import logging
from gym.spaces import Box, Discrete
@@ -20,6 +20,7 @@ import numpy as np
logger = logging.getLogger(__name__)
tf = try_import_tf()
tfp = try_import_tfp()
class MADDPGPostprocessing(object):
@@ -349,7 +350,6 @@ class MADDPGTFPolicy(MADDPGPostprocessing, TFPolicy):
hiddens,
activation=None,
scope=None):
from tensorflow.contrib.distributions import RelaxedOneHotCategorical
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE) as scope:
if self.config["use_state_preprocessor"]:
model = ModelCatalog.get_model({
@@ -365,7 +365,7 @@ class MADDPGTFPolicy(MADDPGPostprocessing, TFPolicy):
out = tf.layers.dense(out, units=hidden, activation=activation)
feature = tf.layers.dense(
out, units=act_space.shape[0], activation=None)
sampler = RelaxedOneHotCategorical(
sampler = tfp.distributions.RelaxedOneHotCategorical(
temperature=1.0, logits=feature).sample()
return sampler, feature, model, tf.global_variables(scope.name)
rllib/examples/custom_keras_rnn_model.py
+1 -1
View File
@@ -47,7 +47,7 @@ class MyKerasRNN(RecurrentTFModelV2):
shape=(None, obs_space.shape[0]), name="inputs")
state_in_h = tf.keras.layers.Input(shape=(cell_size, ), name="h")
state_in_c = tf.keras.layers.Input(shape=(cell_size, ), name="c")
seq_in = tf.keras.layers.Input(shape=(), name="seq_in")
seq_in = tf.keras.layers.Input(shape=(), name="seq_in", dtype=tf.int32)
# Preprocess observation with a hidden layer and send to LSTM cell
dense1 = tf.keras.layers.Dense(
rllib/models/tf/recurrent_tf_modelv2.py
+1 -1
View File
@@ -34,7 +34,7 @@ class RecurrentTFModelV2(TFModelV2):
shape=(None, obs_space.shape[0]))
state_in_h = tf.keras.layers.Input(shape=(256, ))
state_in_c = tf.keras.layers.Input(shape=(256, ))
seq_in = tf.keras.layers.Input(shape=())
seq_in = tf.keras.layers.Input(shape=(), dtype=tf.int32)
# Send to LSTM cell
lstm_out, state_h, state_c = tf.keras.layers.LSTM(
rllib/policy/eager_tf_policy.py
+1 -1
View File
@@ -236,7 +236,7 @@ def build_eager_tf_policy(name,
[_flatten_action(action_space.sample())]),
SampleBatch.PREV_REWARDS: tf.convert_to_tensor([0.]),
}, [
tf.convert_to_tensor([s])
tf.convert_to_tensor(np.array([s]))
for s in self.model.get_initial_state()
], tf.convert_to_tensor([1]))