Tianshou/tianshou/policy/modelfree/fqf.py

from dataclasses import dataclass
from typing import Any, Literal, TypeVar, cast

import gymnasium as gym
import numpy as np
import torch
import torch.nn.functional as F

from tianshou.data import Batch, ReplayBuffer, to_numpy
from tianshou.data.types import FQFBatchProtocol, ObsBatchProtocol, RolloutBatchProtocol
from tianshou.policy import DQNPolicy, QRDQNPolicy
from tianshou.policy.base import TLearningRateScheduler
from tianshou.policy.modelfree.qrdqn import QRDQNTrainingStats
from tianshou.utils.net.discrete import FractionProposalNetwork, FullQuantileFunction


@dataclass(kw_only=True)
class FQFTrainingStats(QRDQNTrainingStats):
    quantile_loss: float
    fraction_loss: float
    entropy_loss: float


TFQFTrainingStats = TypeVar("TFQFTrainingStats", bound=FQFTrainingStats)


class FQFPolicy(QRDQNPolicy[TFQFTrainingStats]):
    """Implementation of Fully-parameterized Quantile Function. arXiv:1911.02140.

    :param model: a model following the rules in
        :class:`~tianshou.policy.BasePolicy`. (s -> logits)
    :param optim: a torch.optim for optimizing the model.
    :param fraction_model: a FractionProposalNetwork for
        proposing fractions/quantiles given state.
    :param fraction_optim: a torch.optim for optimizing
        the fraction model above.
    :param action_space: Env's action space.
    :param discount_factor: in [0, 1].
    :param num_fractions: the number of fractions to use.
    :param ent_coef: the coefficient for entropy loss.
    :param estimation_step: the number of steps to look ahead.
    :param target_update_freq: the target network update frequency (0 if
        you do not use the target network).
    :param reward_normalization: normalize the **returns** to Normal(0, 1).
        TODO: rename to return_normalization?
    :param is_double: use double dqn.
    :param clip_loss_grad: clip the gradient of the loss in accordance
        with nature14236; this amounts to using the Huber loss instead of
        the MSE loss.
    :param observation_space: Env's observation space.
    :param lr_scheduler: if not None, will be called in `policy.update()`.

    .. seealso::

        Please refer to :class:`~tianshou.policy.QRDQNPolicy` for more detailed
        explanation.
    """

    def __init__(
        self,
        *,
        model: FullQuantileFunction,
        optim: torch.optim.Optimizer,
        fraction_model: FractionProposalNetwork,
        fraction_optim: torch.optim.Optimizer,
        action_space: gym.spaces.Discrete,
        discount_factor: float = 0.99,
        # TODO: used as num_quantiles in QRDQNPolicy, but num_fractions in FQFPolicy.
        #  Rename? Or at least explain what happens here.
        num_fractions: int = 32,
        ent_coef: float = 0.0,
        estimation_step: int = 1,
        target_update_freq: int = 0,
        reward_normalization: bool = False,
        is_double: bool = True,
        clip_loss_grad: bool = False,
        observation_space: gym.Space | None = None,
        lr_scheduler: TLearningRateScheduler | None = None,
    ) -> None:
        super().__init__(
            model=model,
            optim=optim,
            action_space=action_space,
            discount_factor=discount_factor,
            num_quantiles=num_fractions,
            estimation_step=estimation_step,
            target_update_freq=target_update_freq,
            reward_normalization=reward_normalization,
            is_double=is_double,
            clip_loss_grad=clip_loss_grad,
            observation_space=observation_space,
            lr_scheduler=lr_scheduler,
        )
        self.fraction_model = fraction_model
        self.ent_coef = ent_coef
        self.fraction_optim = fraction_optim

    def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor:
        obs_next_batch = Batch(
            obs=buffer[indices].obs_next,
            info=[None] * len(indices),
        )  # obs_next: s_{t+n}
        if self._target:
            result = self(obs_next_batch)
            act, fractions = result.act, result.fractions
            next_dist = self(obs_next_batch, model="model_old", fractions=fractions).logits
        else:
            next_batch = self(obs_next_batch)
            act = next_batch.act
            next_dist = next_batch.logits
        return next_dist[np.arange(len(act)), act, :]

    # TODO: fix Liskov substitution principle violation
    def forward(  # type: ignore
        self,
        batch: ObsBatchProtocol,
        state: dict | Batch | np.ndarray | None = None,
        model: Literal["model", "model_old"] = "model",
        fractions: Batch | None = None,
        **kwargs: Any,
    ) -> FQFBatchProtocol:
        model = getattr(self, model)
        obs = batch.obs
        # TODO: this is convoluted! See also other places where this is done
        obs_next = obs.obs if hasattr(obs, "obs") else obs
        if fractions is None:
            (logits, fractions, quantiles_tau), hidden = model(
                obs_next,
                propose_model=self.fraction_model,
                state=state,
                info=batch.info,
            )
        else:
            (logits, _, quantiles_tau), hidden = model(
                obs_next,
                propose_model=self.fraction_model,
                fractions=fractions,
                state=state,
                info=batch.info,
            )
        weighted_logits = (fractions.taus[:, 1:] - fractions.taus[:, :-1]).unsqueeze(1) * logits
        q = DQNPolicy.compute_q_value(self, weighted_logits.sum(2), getattr(obs, "mask", None))
        if self.max_action_num is None:  # type: ignore
            # TODO: see same thing in DQNPolicy! Also reduce code duplication.
            self.max_action_num = q.shape[1]
        act = to_numpy(q.max(dim=1)[1])
        result = Batch(
            logits=logits,
            act=act,
            state=hidden,
            fractions=fractions,
            quantiles_tau=quantiles_tau,
        )
        return cast(FQFBatchProtocol, result)

    def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> TFQFTrainingStats:
        if self._target and self._iter % self.freq == 0:
            self.sync_weight()
        weight = batch.pop("weight", 1.0)
        out = self(batch)
        curr_dist_orig = out.logits
        taus, tau_hats = out.fractions.taus, out.fractions.tau_hats
        act = batch.act
        curr_dist = curr_dist_orig[np.arange(len(act)), act, :].unsqueeze(2)
        target_dist = batch.returns.unsqueeze(1)
        # calculate each element's difference between curr_dist and target_dist
        dist_diff = F.smooth_l1_loss(target_dist, curr_dist, reduction="none")
        huber_loss = (
            (
                dist_diff
                * (tau_hats.unsqueeze(2) - (target_dist - curr_dist).detach().le(0.0).float()).abs()
            )
            .sum(-1)
            .mean(1)
        )
        quantile_loss = (huber_loss * weight).mean()
        # ref: https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/
        # blob/master/fqf_iqn_qrdqn/agent/qrdqn_agent.py L130
        batch.weight = dist_diff.detach().abs().sum(-1).mean(1)  # prio-buffer
        # calculate fraction loss
        with torch.no_grad():
            sa_quantile_hats = curr_dist_orig[np.arange(len(act)), act, :]
            sa_quantiles = out.quantiles_tau[np.arange(len(act)), act, :]
            # ref: https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/
            # blob/master/fqf_iqn_qrdqn/agent/fqf_agent.py L169
            values_1 = sa_quantiles - sa_quantile_hats[:, :-1]
            signs_1 = sa_quantiles > torch.cat(
                [sa_quantile_hats[:, :1], sa_quantiles[:, :-1]],
                dim=1,
            )

            values_2 = sa_quantiles - sa_quantile_hats[:, 1:]
            signs_2 = sa_quantiles < torch.cat(
                [sa_quantiles[:, 1:], sa_quantile_hats[:, -1:]],
                dim=1,
            )

            gradient_of_taus = torch.where(signs_1, values_1, -values_1) + torch.where(
                signs_2,
                values_2,
                -values_2,
            )
        fraction_loss = (gradient_of_taus * taus[:, 1:-1]).sum(1).mean()
        # calculate entropy loss
        entropy_loss = out.fractions.entropies.mean()
        fraction_entropy_loss = fraction_loss - self.ent_coef * entropy_loss
        self.fraction_optim.zero_grad()
        fraction_entropy_loss.backward(retain_graph=True)
        self.fraction_optim.step()
        self.optim.zero_grad()
        quantile_loss.backward()
        self.optim.step()
        self._iter += 1

        return FQFTrainingStats(  # type: ignore[return-value]
            loss=quantile_loss.item() + fraction_entropy_loss.item(),
            quantile_loss=quantile_loss.item(),
            fraction_loss=fraction_loss.item(),
            entropy_loss=entropy_loss.item(),
        )