134 lines
5.1 KiB
Python
134 lines
5.1 KiB
Python
import warnings
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from dataclasses import dataclass
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from typing import Any, Generic, TypeVar
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import gymnasium as gym
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import numpy as np
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import torch
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import torch.nn.functional as F
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from tianshou.data import Batch, ReplayBuffer
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from tianshou.data.types import RolloutBatchProtocol
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from tianshou.policy import DQNPolicy
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from tianshou.policy.base import TLearningRateScheduler
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from tianshou.policy.modelfree.dqn import DQNTrainingStats
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@dataclass(kw_only=True)
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class QRDQNTrainingStats(DQNTrainingStats):
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pass
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TQRDQNTrainingStats = TypeVar("TQRDQNTrainingStats", bound=QRDQNTrainingStats)
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class QRDQNPolicy(DQNPolicy[TQRDQNTrainingStats], Generic[TQRDQNTrainingStats]):
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"""Implementation of Quantile Regression Deep Q-Network. arXiv:1710.10044.
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:param model: a model following the rules (s -> action_values_BA)
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:param optim: a torch.optim for optimizing the model.
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:param action_space: Env's action space.
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:param discount_factor: in [0, 1].
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:param num_quantiles: the number of quantile midpoints in the inverse
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cumulative distribution function of the value.
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:param estimation_step: the number of steps to look ahead.
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:param target_update_freq: the target network update frequency (0 if
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you do not use the target network).
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:param reward_normalization: normalize the **returns** to Normal(0, 1).
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TODO: rename to return_normalization?
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:param is_double: use double dqn.
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:param clip_loss_grad: clip the gradient of the loss in accordance
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with nature14236; this amounts to using the Huber loss instead of
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the MSE loss.
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:param observation_space: Env's observation space.
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:param lr_scheduler: if not None, will be called in `policy.update()`.
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.. seealso::
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Please refer to :class:`~tianshou.policy.DQNPolicy` for more detailed
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explanation.
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"""
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def __init__(
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self,
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*,
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model: torch.nn.Module,
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optim: torch.optim.Optimizer,
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action_space: gym.spaces.Discrete,
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discount_factor: float = 0.99,
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num_quantiles: int = 200,
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estimation_step: int = 1,
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target_update_freq: int = 0,
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reward_normalization: bool = False,
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is_double: bool = True,
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clip_loss_grad: bool = False,
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observation_space: gym.Space | None = None,
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lr_scheduler: TLearningRateScheduler | None = None,
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) -> None:
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assert num_quantiles > 1, f"num_quantiles should be greater than 1 but got: {num_quantiles}"
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super().__init__(
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model=model,
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optim=optim,
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action_space=action_space,
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discount_factor=discount_factor,
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estimation_step=estimation_step,
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target_update_freq=target_update_freq,
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reward_normalization=reward_normalization,
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is_double=is_double,
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clip_loss_grad=clip_loss_grad,
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observation_space=observation_space,
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lr_scheduler=lr_scheduler,
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)
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self.num_quantiles = num_quantiles
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tau = torch.linspace(0, 1, self.num_quantiles + 1)
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self.tau_hat = torch.nn.Parameter(
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((tau[:-1] + tau[1:]) / 2).view(1, -1, 1),
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requires_grad=False,
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)
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warnings.filterwarnings("ignore", message="Using a target size")
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def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor:
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obs_next_batch = Batch(
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obs=buffer[indices].obs_next,
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info=[None] * len(indices),
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) # obs_next: s_{t+n}
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if self._target:
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act = self(obs_next_batch).act
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next_dist = self(obs_next_batch, model="model_old").logits
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else:
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next_batch = self(obs_next_batch)
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act = next_batch.act
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next_dist = next_batch.logits
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return next_dist[np.arange(len(act)), act, :]
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def compute_q_value(self, logits: torch.Tensor, mask: np.ndarray | None) -> torch.Tensor:
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return super().compute_q_value(logits.mean(2), mask)
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def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> TQRDQNTrainingStats:
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# set policy in train mode
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self.train()
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if self._target and self._iter % self.freq == 0:
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self.sync_weight()
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self.optim.zero_grad()
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weight = batch.pop("weight", 1.0)
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curr_dist = self(batch).logits
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act = batch.act
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curr_dist = curr_dist[np.arange(len(act)), act, :].unsqueeze(2)
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target_dist = batch.returns.unsqueeze(1)
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# calculate each element's difference between curr_dist and target_dist
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dist_diff = F.smooth_l1_loss(target_dist, curr_dist, reduction="none")
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huber_loss = (
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(dist_diff * (self.tau_hat - (target_dist - curr_dist).detach().le(0.0).float()).abs())
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.sum(-1)
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.mean(1)
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)
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loss = (huber_loss * weight).mean()
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# ref: https://github.com/ku2482/fqf-iqn-qrdqn.pytorch/
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# blob/master/fqf_iqn_qrdqn/agent/qrdqn_agent.py L130
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batch.weight = dist_diff.detach().abs().sum(-1).mean(1) # prio-buffer
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loss.backward()
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self.optim.step()
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self._iter += 1
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return QRDQNTrainingStats(loss=loss.item()) # type: ignore[return-value]
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