ray/rllib/agents/ppo/ppo_torch_policy.py

"""
PyTorch policy class used for PPO.
"""
import gym
import logging
from typing import Dict, List, Type, Union

import ray
from ray.rllib.agents.ppo.ppo_tf_policy import setup_config
from ray.rllib.evaluation.postprocessing import compute_gae_for_sample_batch, \
    Postprocessing
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.policy_template import build_policy_class
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.torch_policy import EntropyCoeffSchedule, \
    LearningRateSchedule
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.torch_ops import apply_grad_clipping, \
    explained_variance, sequence_mask
from ray.rllib.utils.typing import TensorType, TrainerConfigDict

torch, nn = try_import_torch()

logger = logging.getLogger(__name__)


def ppo_surrogate_loss(
        policy: Policy, model: ModelV2,
        dist_class: Type[TorchDistributionWrapper],
        train_batch: SampleBatch) -> Union[TensorType, List[TensorType]]:
    """Constructs the loss for Proximal Policy Objective.

    Args:
        policy (Policy): The Policy to calculate the loss for.
        model (ModelV2): The Model to calculate the loss for.
        dist_class (Type[ActionDistribution]: The action distr. class.
        train_batch (SampleBatch): The training data.

    Returns:
        Union[TensorType, List[TensorType]]: A single loss tensor or a list
            of loss tensors.
    """
    logits, state = model(train_batch)
    curr_action_dist = dist_class(logits, model)

    # RNN case: Mask away 0-padded chunks at end of time axis.
    if state:
        B = len(train_batch["seq_lens"])
        max_seq_len = logits.shape[0] // B
        mask = sequence_mask(
            train_batch["seq_lens"],
            max_seq_len,
            time_major=model.is_time_major())
        mask = torch.reshape(mask, [-1])
        num_valid = torch.sum(mask)

        def reduce_mean_valid(t):
            return torch.sum(t[mask]) / num_valid

    # non-RNN case: No masking.
    else:
        mask = None
        reduce_mean_valid = torch.mean

    prev_action_dist = dist_class(train_batch[SampleBatch.ACTION_DIST_INPUTS],
                                  model)

    logp_ratio = torch.exp(
        curr_action_dist.logp(train_batch[SampleBatch.ACTIONS]) -
        train_batch[SampleBatch.ACTION_LOGP])
    action_kl = prev_action_dist.kl(curr_action_dist)
    mean_kl = reduce_mean_valid(action_kl)

    curr_entropy = curr_action_dist.entropy()
    mean_entropy = reduce_mean_valid(curr_entropy)

    surrogate_loss = torch.min(
        train_batch[Postprocessing.ADVANTAGES] * logp_ratio,
        train_batch[Postprocessing.ADVANTAGES] * torch.clamp(
            logp_ratio, 1 - policy.config["clip_param"],
            1 + policy.config["clip_param"]))
    mean_policy_loss = reduce_mean_valid(-surrogate_loss)

    # Compute a value function loss.
    if policy.config["use_critic"]:
        prev_value_fn_out = train_batch[SampleBatch.VF_PREDS]
        value_fn_out = model.value_function()
        vf_loss1 = torch.pow(
            value_fn_out - train_batch[Postprocessing.VALUE_TARGETS], 2.0)
        vf_clipped = prev_value_fn_out + torch.clamp(
            value_fn_out - prev_value_fn_out, -policy.config["vf_clip_param"],
            policy.config["vf_clip_param"])
        vf_loss2 = torch.pow(
            vf_clipped - train_batch[Postprocessing.VALUE_TARGETS], 2.0)
        vf_loss = torch.max(vf_loss1, vf_loss2)
        mean_vf_loss = reduce_mean_valid(vf_loss)
    # Ignore the value function.
    else:
        vf_loss = mean_vf_loss = 0.0

    total_loss = reduce_mean_valid(-surrogate_loss +
                                   policy.kl_coeff * action_kl +
                                   policy.config["vf_loss_coeff"] * vf_loss -
                                   policy.entropy_coeff * curr_entropy)

    # Store stats in policy for stats_fn.
    policy._total_loss = total_loss
    policy._mean_policy_loss = mean_policy_loss
    policy._mean_vf_loss = mean_vf_loss
    policy._vf_explained_var = explained_variance(
        train_batch[Postprocessing.VALUE_TARGETS], model.value_function())
    policy._mean_entropy = mean_entropy
    policy._mean_kl = mean_kl

    return total_loss


def kl_and_loss_stats(policy: Policy,
                      train_batch: SampleBatch) -> Dict[str, TensorType]:
    """Stats function for PPO. Returns a dict with important KL and loss stats.

    Args:
        policy (Policy): The Policy to generate stats for.
        train_batch (SampleBatch): The SampleBatch (already) used for training.

    Returns:
        Dict[str, TensorType]: The stats dict.
    """
    return {
        "cur_kl_coeff": policy.kl_coeff,
        "cur_lr": policy.cur_lr,
        "total_loss": policy._total_loss,
        "policy_loss": policy._mean_policy_loss,
        "vf_loss": policy._mean_vf_loss,
        "vf_explained_var": policy._vf_explained_var,
        "kl": policy._mean_kl,
        "entropy": policy._mean_entropy,
        "entropy_coeff": policy.entropy_coeff,
    }


def vf_preds_fetches(
        policy: Policy, input_dict: Dict[str, TensorType],
        state_batches: List[TensorType], model: ModelV2,
        action_dist: TorchDistributionWrapper) -> Dict[str, TensorType]:
    """Defines extra fetches per action computation.

    Args:
        policy (Policy): The Policy to perform the extra action fetch on.
        input_dict (Dict[str, TensorType]): The input dict used for the action
            computing forward pass.
        state_batches (List[TensorType]): List of state tensors (empty for
            non-RNNs).
        model (ModelV2): The Model object of the Policy.
        action_dist (TorchDistributionWrapper): The instantiated distribution
            object, resulting from the model's outputs and the given
            distribution class.

    Returns:
        Dict[str, TensorType]: Dict with extra tf fetches to perform per
            action computation.
    """
    # Return value function outputs. VF estimates will hence be added to the
    # SampleBatches produced by the sampler(s) to generate the train batches
    # going into the loss function.
    return {
        SampleBatch.VF_PREDS: model.value_function(),
    }


class KLCoeffMixin:
    """Assigns the `update_kl()` method to the PPOPolicy.

    This is used in PPO's execution plan (see ppo.py) for updating the KL
    coefficient after each learning step based on `config.kl_target` and
    the measured KL value (from the train_batch).
    """

    def __init__(self, config):
        # The current KL value (as python float).
        self.kl_coeff = config["kl_coeff"]
        # Constant target value.
        self.kl_target = config["kl_target"]

    def update_kl(self, sampled_kl):
        # Update the current KL value based on the recently measured value.
        if sampled_kl > 2.0 * self.kl_target:
            self.kl_coeff *= 1.5
        elif sampled_kl < 0.5 * self.kl_target:
            self.kl_coeff *= 0.5
        # Return the current KL value.
        return self.kl_coeff


class ValueNetworkMixin:
    """Assigns the `_value()` method to the PPOPolicy.

    This way, Policy can call `_value()` to get the current VF estimate on a
    single(!) observation (as done in `postprocess_trajectory_fn`).
    Note: When doing this, an actual forward pass is being performed.
    This is different from only calling `model.value_function()`, where
    the result of the most recent forward pass is being used to return an
    already calculated tensor.
    """

    def __init__(self, obs_space, action_space, config):
        # When doing GAE, we need the value function estimate on the
        # observation.
        if config["use_gae"]:
            # Input dict is provided to us automatically via the Model's
            # requirements. It's a single-timestep (last one in trajectory)
            # input_dict.

            def value(**input_dict):
                input_dict = SampleBatch(input_dict)
                input_dict = self._lazy_tensor_dict(input_dict)
                model_out, _ = self.model(input_dict)
                # [0] = remove the batch dim.
                return self.model.value_function()[0].item()

        # When not doing GAE, we do not require the value function's output.
        else:

            def value(*args, **kwargs):
                return 0.0

        self._value = value


def setup_mixins(policy: Policy, obs_space: gym.spaces.Space,
                 action_space: gym.spaces.Space,
                 config: TrainerConfigDict) -> None:
    """Call all mixin classes' constructors before PPOPolicy initialization.

    Args:
        policy (Policy): The Policy object.
        obs_space (gym.spaces.Space): The Policy's observation space.
        action_space (gym.spaces.Space): The Policy's action space.
        config (TrainerConfigDict): The Policy's config.
    """
    ValueNetworkMixin.__init__(policy, obs_space, action_space, config)
    KLCoeffMixin.__init__(policy, config)
    EntropyCoeffSchedule.__init__(policy, config["entropy_coeff"],
                                  config["entropy_coeff_schedule"])
    LearningRateSchedule.__init__(policy, config["lr"], config["lr_schedule"])


# Build a child class of `TorchPolicy`, given the custom functions defined
# above.
PPOTorchPolicy = build_policy_class(
    name="PPOTorchPolicy",
    framework="torch",
    get_default_config=lambda: ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG,
    loss_fn=ppo_surrogate_loss,
    stats_fn=kl_and_loss_stats,
    extra_action_out_fn=vf_preds_fetches,
    postprocess_fn=compute_gae_for_sample_batch,
    extra_grad_process_fn=apply_grad_clipping,
    before_init=setup_config,
    before_loss_init=setup_mixins,
    mixins=[
        LearningRateSchedule, EntropyCoeffSchedule, KLCoeffMixin,
        ValueNetworkMixin
    ],
)