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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.policy.policy_template import build_policy_class
from ray.rllib.policy.sample_batch import SampleBatch
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({SampleBatch.CUR_OBS: train_batch[SampleBatch.CUR_OBS]})
action_dist = dist_class(logits, model)
log_probs = action_dist.logp(train_batch[SampleBatch.ACTIONS])
return -train_batch[SampleBatch.REWARDS].dot(log_probs)
# <class 'ray.rllib.policy.torch_policy_template.MyTorchPolicy'>
MyTorchPolicy = build_policy_class(
name="MyTorchPolicy", framework="torch", loss_fn=policy_gradient_loss)
# <class 'ray.rllib.agents.trainer_template.MyCustomTrainer'>
MyTrainer = build_trainer(
name="MyCustomTrainer",
default_policy=MyTorchPolicy,
)
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": "torch",
})