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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import ray
from ray import tune
from ray.rllib.agents.trainer_template import build_trainer
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.torch_policy_template import build_torch_policy
parser = argparse.ArgumentParser()
parser.add_argument("--iters", type=int, default=200)
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_torch_policy(
name="MyTorchPolicy", loss_fn=policy_gradient_loss)
# <class 'ray.rllib.agents.trainer_template.MyCustomTrainer'>
MyTrainer = build_trainer(
name="MyCustomTrainer",
default_policy=MyTorchPolicy,
)
if __name__ == "__main__":
ray.init()
args = parser.parse_args()
tune.run(
MyTrainer,
stop={"training_iteration": args.iters},
config={
"env": "CartPole-v0",
"num_workers": 2,
})
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