3a1bc18add
This PR adds a new method for getting actions from an env's observation and info. This is useful for standard inference and stands in contrast to batch-based methods that are currently used in training and evaluation. Without this, users have to do some kind of gymnastics to actually perform inference with a trained policy. I have also added a test for the new method. In future PRs, this method should be included in the examples (in the the "watch" section). To add this required improving multiple typing things and, importantly, _simplifying the signature of `forward` in many policies!_ This is a **breaking change**, but it will likely affect no users. The `input` parameter of forward was a rather hacky mechanism, I believe it is good that it's gone now. It will also help with #948 . The main functional change is the addition of `compute_action` to `BasePolicy`. Other minor changes: - improvements in typing - updated PR and Issue templates - Improved handling of `max_action_num` Closes #981
208 lines
8.3 KiB
Python
208 lines
8.3 KiB
Python
from typing import Any, Literal, 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.utils.net.discrete import FractionProposalNetwork, FullQuantileFunction
|
|
|
|
|
|
class FQFPolicy(QRDQNPolicy):
|
|
"""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) -> 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(0.0).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(),
|
|
}
|