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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import random
import ray
from ray import tune
from ray.rllib.agents.trainer_template import build_trainer
from ray.rllib.models import Model, ModelCatalog
from import FullyConnectedNetwork
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.tf_policy_template import build_tf_policy
from ray.rllib.utils import try_import_tf
tf = try_import_tf()
parser = argparse.ArgumentParser()
parser.add_argument("--iters", type=int, default=200)
class EagerModel(Model):
"""Example of using embedded eager execution in a custom model.
This shows how to use tf.py_function() to execute a snippet of TF code
in eager mode. Here the `self.forward_eager` method just prints out
the intermediate tensor for debug purposes, but you can in general
perform any TF eager operation in tf.py_function().
def _build_layers_v2(self, input_dict, num_outputs, options):
self.fcnet = FullyConnectedNetwork(input_dict, self.obs_space,
self.action_space, num_outputs,
feature_out = tf.py_function(self.forward_eager,
[self.fcnet.last_layer], tf.float32)
with tf.control_dependencies([feature_out]):
return tf.identity(self.fcnet.outputs), feature_out
def forward_eager(self, feature_layer):
assert tf.executing_eagerly()
if random.random() > 0.99:
print("Eagerly printing the feature layer mean value",
return feature_layer
def policy_gradient_loss(policy, batch_tensors):
"""Example of using embedded eager execution in a custom loss.
Here `compute_penalty` prints the actions and rewards for debugging, and
also computes a (dummy) penalty term to add to the loss.
def compute_penalty(actions, rewards):
assert tf.executing_eagerly()
penalty = tf.reduce_mean(tf.cast(actions, tf.float32))
if random.random() > 0.9:
print("The eagerly computed penalty is", penalty, actions, rewards)
return penalty
actions = batch_tensors[SampleBatch.ACTIONS]
rewards = batch_tensors[SampleBatch.REWARDS]
penalty = tf.py_function(
compute_penalty, [actions, rewards], Tout=tf.float32)
return penalty - tf.reduce_mean(policy.action_dist.logp(actions) * rewards)
# <class 'ray.rllib.policy.tf_policy_template.MyTFPolicy'>
MyTFPolicy = build_tf_policy(
# <class 'ray.rllib.agents.trainer_template.MyCustomTrainer'>
MyTrainer = build_trainer(
if __name__ == "__main__":
args = parser.parse_args()
ModelCatalog.register_custom_model("eager_model", EagerModel)
stop={"training_iteration": args.iters},
"env": "CartPole-v0",
"num_workers": 0,
"model": {
"custom_model": "eager_model"
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