mirror of
https://github.com/vale981/ray
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60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
import argparse
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import random
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import ray
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from ray import tune
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from ray.rllib.agents.trainer_template import build_trainer
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from ray.rllib.models import Model, ModelCatalog
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from ray.rllib.models.tf.fcnet_v1 import FullyConnectedNetwork
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.policy.tf_policy_template import build_tf_policy
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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parser = argparse.ArgumentParser()
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parser.add_argument("--iters", type=int, default=200)
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class EagerModel(Model):
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"""Example of using embedded eager execution in a custom model.
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This shows how to use tf.py_function() to execute a snippet of TF code
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in eager mode. Here the `self.forward_eager` method just prints out
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the intermediate tensor for debug purposes, but you can in general
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perform any TF eager operation in tf.py_function().
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"""
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def _build_layers_v2(self, input_dict, num_outputs, options):
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self.fcnet = FullyConnectedNetwork(input_dict, self.obs_space,
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self.action_space, num_outputs,
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options)
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feature_out = tf.py_function(self.forward_eager,
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[self.fcnet.last_layer], tf.float32)
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with tf.control_dependencies([feature_out]):
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return tf.identity(self.fcnet.outputs), feature_out
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def forward_eager(self, feature_layer):
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assert tf.executing_eagerly()
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if random.random() > 0.99:
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print("Eagerly printing the feature layer mean value",
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tf.reduce_mean(feature_layer))
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return feature_layer
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def policy_gradient_loss(policy, model, dist_class, train_batch):
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"""Example of using embedded eager execution in a custom loss.
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Here `compute_penalty` prints the actions and rewards for debugging, and
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also computes a (dummy) penalty term to add to the loss.
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"""
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def compute_penalty(actions, rewards):
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assert tf.executing_eagerly()
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penalty = tf.reduce_mean(tf.cast(actions, tf.float32))
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if random.random() > 0.9:
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print("The eagerly computed penalty is", penalty, actions, rewards)
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return penalty
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logits, _ = model.from_batch(train_batch)
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action_dist = dist_class(logits, model)
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actions = train_batch[SampleBatch.ACTIONS]
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rewards = train_batch[SampleBatch.REWARDS]
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penalty = tf.py_function(
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compute_penalty, [actions, rewards], Tout=tf.float32)
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return penalty - tf.reduce_mean(action_dist.logp(actions) * rewards)
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# <class 'ray.rllib.policy.tf_policy_template.MyTFPolicy'>
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MyTFPolicy = build_tf_policy(
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name="MyTFPolicy",
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loss_fn=policy_gradient_loss,
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)
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# <class 'ray.rllib.agents.trainer_template.MyCustomTrainer'>
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MyTrainer = build_trainer(
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name="MyCustomTrainer",
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default_policy=MyTFPolicy,
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)
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if __name__ == "__main__":
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ray.init()
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args = parser.parse_args()
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ModelCatalog.register_custom_model("eager_model", EagerModel)
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tune.run(
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MyTrainer,
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stop={"training_iteration": args.iters},
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config={
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"env": "CartPole-v0",
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"num_workers": 0,
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"model": {
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"custom_model": "eager_model"
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},
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})
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