from typing import Any, Dict, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
from tianshou.data import Batch, ReplayBuffer, to_numpy
from tianshou.policy import DQNPolicy, QRDQNPolicy
from tianshou.utils.net.discrete import FractionProposalNetwork, FullQuantileFunction
[docs]class FQFPolicy(QRDQNPolicy):
"""Implementation of Fully-parameterized Quantile Function. arXiv:1911.02140.
:param torch.nn.Module model: a model following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
:param FractionProposalNetwork fraction_model: a FractionProposalNetwork for
proposing fractions/quantiles given state.
:param torch.optim.Optimizer fraction_optim: a torch.optim for optimizing
the fraction model above.
:param float discount_factor: in [0, 1].
:param int num_fractions: the number of fractions to use. Default to 32.
:param float ent_coef: the coefficient for entropy loss. Default to 0.
:param int estimation_step: the number of steps to look ahead. Default to 1.
:param int target_update_freq: the target network update frequency (0 if
you do not use the target network).
:param bool reward_normalization: normalize the reward to Normal(0, 1).
Default to False.
.. 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,
discount_factor: float = 0.99,
num_fractions: int = 32,
ent_coef: float = 0.0,
estimation_step: int = 1,
target_update_freq: int = 0,
reward_normalization: bool = False,
**kwargs: Any,
) -> None:
super().__init__(
model, optim, discount_factor, num_fractions, estimation_step,
target_update_freq, reward_normalization, **kwargs
)
self.propose_model = fraction_model
self._ent_coef = ent_coef
self._fraction_optim = fraction_optim
def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor:
batch = buffer[indices] # batch.obs_next: s_{t+n}
if self._target:
result = self(batch, input="obs_next")
act, fractions = result.act, result.fractions
next_dist = self(
batch, model="model_old", input="obs_next", fractions=fractions
).logits
else:
next_batch = self(batch, input="obs_next")
act = next_batch.act
next_dist = next_batch.logits
next_dist = next_dist[np.arange(len(act)), act, :]
return next_dist # shape: [bsz, num_quantiles]
[docs] def forward(
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
model: str = "model",
input: str = "obs",
fractions: Optional[Batch] = None,
**kwargs: Any,
) -> Batch:
model = getattr(self, model)
obs = batch[input]
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.propose_model,
state=state,
info=batch.info
)
else:
(logits, _, quantiles_tau), hidden = model(
obs_next,
propose_model=self.propose_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 not hasattr(self, "max_action_num"):
self.max_action_num = q.shape[1]
act = to_numpy(q.max(dim=1)[1])
return Batch(
logits=logits,
act=act,
state=hidden,
fractions=fractions,
quantiles_tau=quantiles_tau
)
[docs] def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
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.).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 {
"loss": quantile_loss.item() + fraction_entropy_loss.item(),
"loss/quantile": quantile_loss.item(),
"loss/fraction": fraction_loss.item(),
"loss/entropy": entropy_loss.item()
}