1eb6137645
This is the PR for QR-DQN algorithm: https://arxiv.org/abs/1710.10044 1. add QR-DQN policy in tianshou/policy/modelfree/qrdqn.py. 2. add QR-DQN net in examples/atari/atari_network.py. 3. add QR-DQN atari example in examples/atari/atari_qrdqn.py. 4. add QR-DQN statement in tianshou/policy/init.py. 5. add QR-DQN unit test in test/discrete/test_qrdqn.py. 6. add QR-DQN atari results in examples/atari/results/qrdqn/. 7. add compute_q_value in DQNPolicy and C51Policy for simplify forward function. 8. move `with torch.no_grad():` from `_target_q` to BasePolicy By running "python3 atari_qrdqn.py --task "PongNoFrameskip-v4" --batch-size 64", get best_result': '19.8 ± 0.40', in epoch 8.
95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
import torch
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import warnings
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import numpy as np
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from typing import Any, Dict
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import torch.nn.functional as F
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from tianshou.policy import DQNPolicy
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from tianshou.data import Batch, ReplayBuffer
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class QRDQNPolicy(DQNPolicy):
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"""Implementation of Quantile Regression Deep Q-Network. arXiv:1710.10044.
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:param torch.nn.Module model: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> logits)
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:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
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:param float discount_factor: in [0, 1].
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:param int num_quantiles: the number of quantile midpoints in the inverse
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cumulative distribution function of the value, defaults to 200.
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:param int estimation_step: greater than 1, the number of steps to look
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ahead.
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:param int 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 bool reward_normalization: normalize the reward to Normal(0, 1),
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defaults to False.
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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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model: torch.nn.Module,
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optim: torch.optim.Optimizer,
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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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**kwargs: Any,
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) -> None:
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super().__init__(model, optim, discount_factor, estimation_step,
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target_update_freq, reward_normalization, **kwargs)
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assert num_quantiles > 1, "num_quantiles should be greater than 1"
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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), requires_grad=False)
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warnings.filterwarnings("ignore", message="Using a target size")
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def _target_q(
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self, buffer: ReplayBuffer, indice: np.ndarray
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) -> torch.Tensor:
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batch = buffer[indice] # batch.obs_next: s_{t+n}
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if self._target:
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a = self(batch, input="obs_next").act
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next_dist = self(
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batch, model="model_old", input="obs_next"
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).logits
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else:
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next_b = self(batch, input="obs_next")
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a = next_b.act
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next_dist = next_b.logits
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next_dist = next_dist[np.arange(len(a)), a, :]
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return next_dist # shape: [bsz, num_quantiles]
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def compute_q_value(self, logits: torch.Tensor) -> torch.Tensor:
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"""Compute the q value based on the network's raw output logits."""
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return logits.mean(2)
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def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
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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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u = F.smooth_l1_loss(target_dist, curr_dist, reduction="none")
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huber_loss = (u * (
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self.tau_hat - (target_dist - curr_dist).detach().le(0.).float()
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).abs()).sum(-1).mean(1)
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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 = u.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 {"loss": loss.item()}
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