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