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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import ray
from ray.rllib.evaluation.postprocessing import compute_advantages, \
Postprocessing
from ray.rllib.policy.tf_policy_template import build_tf_policy
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils import try_import_tf
tf = try_import_tf()
# The basic policy gradients loss
def policy_gradient_loss(policy, batch_tensors):
actions = batch_tensors[SampleBatch.ACTIONS]
advantages = batch_tensors[Postprocessing.ADVANTAGES]
return -tf.reduce_mean(policy.action_dist.logp(actions) * advantages)
# This adds the "advantages" column to the sample batch.
def postprocess_advantages(policy,
sample_batch,
other_agent_batches=None,
episode=None):
return compute_advantages(
sample_batch, 0.0, policy.config["gamma"], use_gae=False)
PGTFPolicy = build_tf_policy(
name="PGTFPolicy",
get_default_config=lambda: ray.rllib.agents.pg.pg.DEFAULT_CONFIG,
postprocess_fn=postprocess_advantages,
loss_fn=policy_gradient_loss)
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