ray/rllib/agents/a3c/a3c_tf_policy.py

"""Note: Keep in sync with changes to VTraceTFPolicy."""
from typing import Optional, Dict
import gym

import ray
from ray.rllib.agents.ppo.ppo_tf_policy import ValueNetworkMixin
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.evaluation.postprocessing import compute_gae_for_sample_batch, \
    Postprocessing
from ray.rllib.policy.tf_policy_template import build_tf_policy
from ray.rllib.policy.tf_policy import LearningRateSchedule
from ray.rllib.utils.annotations import Deprecated
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.tf_ops import explained_variance
from ray.rllib.policy.policy import Policy
from ray.rllib.utils.typing import TrainerConfigDict, TensorType, \
    PolicyID, LocalOptimizer, ModelGradients
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.evaluation import MultiAgentEpisode

tf1, tf, tfv = try_import_tf()


@Deprecated(
    old="rllib.agents.a3c.a3c_tf_policy.postprocess_advantages",
    new="rllib.evaluation.postprocessing.compute_gae_for_sample_batch",
    error=False)
def postprocess_advantages(
        policy: Policy,
        sample_batch: SampleBatch,
        other_agent_batches: Optional[Dict[PolicyID, SampleBatch]] = None,
        episode: Optional[MultiAgentEpisode] = None) -> SampleBatch:

    return compute_gae_for_sample_batch(policy, sample_batch,
                                        other_agent_batches, episode)


class A3CLoss:
    def __init__(self,
                 action_dist: ActionDistribution,
                 actions: TensorType,
                 advantages: TensorType,
                 v_target: TensorType,
                 vf: TensorType,
                 valid_mask: TensorType,
                 vf_loss_coeff: float = 0.5,
                 entropy_coeff: float = 0.01,
                 use_critic: bool = True):
        log_prob = action_dist.logp(actions)

        # The "policy gradients" loss
        self.pi_loss = -tf.reduce_sum(
            tf.boolean_mask(log_prob * advantages, valid_mask))

        delta = tf.boolean_mask(vf - v_target, valid_mask)

        # Compute a value function loss.
        if use_critic:
            self.vf_loss = 0.5 * tf.reduce_sum(tf.math.square(delta))
        # Ignore the value function.
        else:
            self.vf_loss = tf.constant(0.0)

        self.entropy = tf.reduce_sum(
            tf.boolean_mask(action_dist.entropy(), valid_mask))

        self.total_loss = (self.pi_loss + self.vf_loss * vf_loss_coeff -
                           self.entropy * entropy_coeff)


def actor_critic_loss(policy: Policy, model: ModelV2,
                      dist_class: ActionDistribution,
                      train_batch: SampleBatch) -> TensorType:
    model_out, _ = model.from_batch(train_batch)
    action_dist = dist_class(model_out, model)
    if policy.is_recurrent():
        max_seq_len = tf.reduce_max(train_batch[SampleBatch.SEQ_LENS])
        mask = tf.sequence_mask(train_batch[SampleBatch.SEQ_LENS], max_seq_len)
        mask = tf.reshape(mask, [-1])
    else:
        mask = tf.ones_like(train_batch[SampleBatch.REWARDS])
    policy.loss = A3CLoss(action_dist, train_batch[SampleBatch.ACTIONS],
                          train_batch[Postprocessing.ADVANTAGES],
                          train_batch[Postprocessing.VALUE_TARGETS],
                          model.value_function(), mask,
                          policy.config["vf_loss_coeff"],
                          policy.config["entropy_coeff"],
                          policy.config.get("use_critic", True))
    return policy.loss.total_loss


def add_value_function_fetch(policy: Policy) -> Dict[str, TensorType]:
    return {SampleBatch.VF_PREDS: policy.model.value_function()}


def stats(policy: Policy, train_batch: SampleBatch) -> Dict[str, TensorType]:
    return {
        "cur_lr": tf.cast(policy.cur_lr, tf.float64),
        "policy_loss": policy.loss.pi_loss,
        "policy_entropy": policy.loss.entropy,
        "var_gnorm": tf.linalg.global_norm(
            list(policy.model.trainable_variables())),
        "vf_loss": policy.loss.vf_loss,
    }


def grad_stats(policy: Policy, train_batch: SampleBatch,
               grads: ModelGradients) -> Dict[str, TensorType]:
    return {
        "grad_gnorm": tf.linalg.global_norm(grads),
        "vf_explained_var": explained_variance(
            train_batch[Postprocessing.VALUE_TARGETS],
            policy.model.value_function()),
    }


def clip_gradients(policy: Policy, optimizer: LocalOptimizer,
                   loss: TensorType) -> ModelGradients:
    grads_and_vars = optimizer.compute_gradients(
        loss, policy.model.trainable_variables())
    grads = [g for (g, v) in grads_and_vars]
    grads, _ = tf.clip_by_global_norm(grads, policy.config["grad_clip"])
    clipped_grads = list(zip(grads, policy.model.trainable_variables()))
    return clipped_grads


def setup_mixins(policy: Policy, obs_space: gym.spaces.Space,
                 action_space: gym.spaces.Space,
                 config: TrainerConfigDict) -> None:
    ValueNetworkMixin.__init__(policy, obs_space, action_space, config)
    LearningRateSchedule.__init__(policy, config["lr"], config["lr_schedule"])


A3CTFPolicy = build_tf_policy(
    name="A3CTFPolicy",
    get_default_config=lambda: ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG,
    loss_fn=actor_critic_loss,
    stats_fn=stats,
    grad_stats_fn=grad_stats,
    compute_gradients_fn=clip_gradients,
    postprocess_fn=compute_gae_for_sample_batch,
    extra_action_out_fn=add_value_function_fetch,
    before_loss_init=setup_mixins,
    mixins=[ValueNetworkMixin, LearningRateSchedule])