mirror of
https://github.com/vale981/ray
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124 lines
4 KiB
Python
124 lines
4 KiB
Python
import argparse
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import os
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import random
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import ray
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from ray.rllib.agents.trainer_template import build_trainer
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from ray.rllib.examples.models.eager_model import EagerModel
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from ray.rllib.models import ModelCatalog
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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.framework import try_import_tf
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from ray.rllib.utils.test_utils import check_learning_achieved
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from ray import tune
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# Always import tensorflow using this utility function:
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tf1, tf, tfv = try_import_tf()
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# tf1: The installed tf1.x package OR the tf.compat.v1 module within
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# a 2.x tf installation.
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# tf: The installed tf package (whatever tf version was installed).
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# tfv: The tf version int (either 1 or 2).
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# To enable eager mode, do:
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# >> tf1.enable_eager_execution()
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# >> x = tf.Variable(0.0)
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# >> x.numpy()
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# 0.0
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# RLlib will automatically enable eager mode, if you specify your "framework"
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# config key to be either "tfe" or "tf2".
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# If you would like to remain in tf static-graph mode, but still use tf2.x's
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# new APIs (some of which are not supported by tf1.x), specify your "framework"
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# as "tf" and check for the version (tfv) to be 2:
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# Example:
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# >> def dense(x, W, b):
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# .. return tf.nn.sigmoid(tf.matmul(x, W) + b)
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#
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# >> @tf.function
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# >> def multilayer_perceptron(x, w0, b0):
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# .. return dense(x, w0, b0)
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# Also be careful to distinguish between tf1 and tf in your code. For example,
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# to create a placeholder:
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# >> tf1.placeholder(tf.float32, (2, )) # <- must use `tf1` here
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--as-test",
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action="store_true",
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help="Whether this script should be run as a test: --stop-reward must "
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"be achieved within --stop-timesteps AND --stop-iters.",
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)
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parser.add_argument(
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"--stop-iters", type=int, default=200, help="Number of iterations to train."
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)
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parser.add_argument(
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"--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
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)
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parser.add_argument(
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"--stop-reward", type=float, default=150.0, help="Reward at which we stop training."
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)
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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(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(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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config = {
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"env": "CartPole-v0",
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"num_workers": 0,
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"model": {"custom_model": "eager_model"},
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# Alternatively, use "tf2" here for enforcing TF version 2.x.
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"framework": "tfe",
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}
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stop = {
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"timesteps_total": args.stop_timesteps,
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"training_iteration": args.stop_iters,
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"episode_reward_mean": args.stop_reward,
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}
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results = tune.run(MyTrainer, stop=stop, config=config, verbose=1)
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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