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import argparse
import os
import ray
from ray import tune
from ray.rllib.agents.trainer_template import build_trainer
from ray.rllib.evaluation.postprocessing import discount_cumsum
from ray.rllib.policy.tf_policy_template import build_tf_policy
from ray.rllib.utils.framework import try_import_tf
tf1, tf, tfv = try_import_tf()
parser = argparse.ArgumentParser()
parser.add_argument("--stop-iters", type=int, default=200)
parser.add_argument("--num-cpus", type=int, default=0)
def policy_gradient_loss(policy, model, dist_class, train_batch):
logits, _ = model(train_batch)
action_dist = dist_class(logits, model)
return -tf.reduce_mean(
action_dist.logp(train_batch["actions"]) * train_batch["returns"])
def calculate_advantages(policy,
sample_batch,
other_agent_batches=None,
episode=None):
sample_batch["returns"] = discount_cumsum(sample_batch["rewards"], 0.99)
return sample_batch
# <class 'ray.rllib.policy.tf_policy_template.MyTFPolicy'>
MyTFPolicy = build_tf_policy(
name="MyTFPolicy",
loss_fn=policy_gradient_loss,
postprocess_fn=calculate_advantages,
)
# <class 'ray.rllib.agents.trainer_template.MyCustomTrainer'>
MyTrainer = build_trainer(
name="MyCustomTrainer",
default_policy=MyTFPolicy,
)
if __name__ == "__main__":
args = parser.parse_args()
ray.init(num_cpus=args.num_cpus or None)
tune.run(
MyTrainer,
stop={"training_iteration": args.stop_iters},
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": 2,
"framework": "tf",
})