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.
107 lines
4.2 KiB
Python
107 lines
4.2 KiB
Python
import torch
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import numpy as np
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from typing import Any, Dict
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from tianshou.policy import DQNPolicy
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from tianshou.data import Batch, ReplayBuffer
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class C51Policy(DQNPolicy):
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"""Implementation of Categorical Deep Q-Network. arXiv:1707.06887.
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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_atoms: the number of atoms in the support set of the
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value distribution, defaults to 51.
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:param float v_min: the value of the smallest atom in the support set,
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defaults to -10.0.
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:param float v_max: the value of the largest atom in the support set,
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defaults to 10.0.
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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_atoms: int = 51,
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v_min: float = -10.0,
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v_max: float = 10.0,
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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_atoms > 1, "num_atoms should be greater than 1"
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assert v_min < v_max, "v_max should be larger than v_min"
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self._num_atoms = num_atoms
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self._v_min = v_min
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self._v_max = v_max
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self.support = torch.nn.Parameter(
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torch.linspace(self._v_min, self._v_max, self._num_atoms),
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requires_grad=False,
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)
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self.delta_z = (v_max - v_min) / (num_atoms - 1)
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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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return self.support.repeat(len(indice), 1) # shape: [bsz, num_atoms]
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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 * self.support).sum(2)
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def _target_dist(self, batch: Batch) -> torch.Tensor:
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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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target_support = batch.returns.clamp(self._v_min, self._v_max)
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# An amazing trick for calculating the projection gracefully.
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# ref: https://github.com/ShangtongZhang/DeepRL
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target_dist = (1 - (target_support.unsqueeze(1) -
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self.support.view(1, -1, 1)).abs() / self.delta_z
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).clamp(0, 1) * next_dist.unsqueeze(1)
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return target_dist.sum(-1)
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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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with torch.no_grad():
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target_dist = self._target_dist(batch)
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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, :]
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cross_entropy = - (target_dist * torch.log(curr_dist + 1e-8)).sum(1)
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loss = (cross_entropy * weight).mean()
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# ref: https://github.com/Kaixhin/Rainbow/blob/master/agent.py L94-100
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batch.weight = cross_entropy.detach() # 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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