150d0ec51b
This is the third PR of 6 commits mentioned in #274, which features refactor of Collector to fix #245. You can check #274 for more detail. Things changed in this PR: 1. refactor collector to be more cleaner, split AsyncCollector to support asyncvenv; 2. change buffer.add api to add(batch, bffer_ids); add several types of buffer (VectorReplayBuffer, PrioritizedVectorReplayBuffer, etc.) 3. add policy.exploration_noise(act, batch) -> act 4. small change in BasePolicy.compute_*_returns 5. move reward_metric from collector to trainer 6. fix np.asanyarray issue (different version's numpy will result in different output) 7. flake8 maxlength=88 8. polish docs and fix test Co-authored-by: n+e <trinkle23897@gmail.com>
105 lines
4.1 KiB
Python
105 lines
4.1 KiB
Python
import torch
|
|
import numpy as np
|
|
from typing import Any, Dict
|
|
|
|
from tianshou.policy import DQNPolicy
|
|
from tianshou.data import Batch, ReplayBuffer
|
|
|
|
|
|
class C51Policy(DQNPolicy):
|
|
"""Implementation of Categorical Deep Q-Network. arXiv:1707.06887.
|
|
|
|
: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 float discount_factor: in [0, 1].
|
|
:param int num_atoms: the number of atoms in the support set of the
|
|
value distribution, defaults to 51.
|
|
:param float v_min: the value of the smallest atom in the support set,
|
|
defaults to -10.0.
|
|
:param float v_max: the value of the largest atom in the support set,
|
|
defaults to 10.0.
|
|
:param int estimation_step: greater than 1, the number of steps to look
|
|
ahead.
|
|
: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),
|
|
defaults to False.
|
|
|
|
.. seealso::
|
|
|
|
Please refer to :class:`~tianshou.policy.DQNPolicy` for more detailed
|
|
explanation.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model: torch.nn.Module,
|
|
optim: torch.optim.Optimizer,
|
|
discount_factor: float = 0.99,
|
|
num_atoms: int = 51,
|
|
v_min: float = -10.0,
|
|
v_max: float = 10.0,
|
|
estimation_step: int = 1,
|
|
target_update_freq: int = 0,
|
|
reward_normalization: bool = False,
|
|
**kwargs: Any,
|
|
) -> None:
|
|
super().__init__(model, optim, discount_factor, estimation_step,
|
|
target_update_freq, reward_normalization, **kwargs)
|
|
assert num_atoms > 1, "num_atoms should be greater than 1"
|
|
assert v_min < v_max, "v_max should be larger than v_min"
|
|
self._num_atoms = num_atoms
|
|
self._v_min = v_min
|
|
self._v_max = v_max
|
|
self.support = torch.nn.Parameter(
|
|
torch.linspace(self._v_min, self._v_max, self._num_atoms),
|
|
requires_grad=False,
|
|
)
|
|
self.delta_z = (v_max - v_min) / (num_atoms - 1)
|
|
|
|
def _target_q(
|
|
self, buffer: ReplayBuffer, indice: np.ndarray
|
|
) -> torch.Tensor:
|
|
return self.support.repeat(len(indice), 1) # shape: [bsz, num_atoms]
|
|
|
|
def compute_q_value(self, logits: torch.Tensor) -> torch.Tensor:
|
|
"""Compute the q value based on the network's raw output logits."""
|
|
return (logits * self.support).sum(2)
|
|
|
|
def _target_dist(self, batch: Batch) -> torch.Tensor:
|
|
if self._target:
|
|
a = self(batch, input="obs_next").act
|
|
next_dist = self(batch, model="model_old", input="obs_next").logits
|
|
else:
|
|
next_b = self(batch, input="obs_next")
|
|
a = next_b.act
|
|
next_dist = next_b.logits
|
|
next_dist = next_dist[np.arange(len(a)), a, :]
|
|
target_support = batch.returns.clamp(self._v_min, self._v_max)
|
|
# An amazing trick for calculating the projection gracefully.
|
|
# ref: https://github.com/ShangtongZhang/DeepRL
|
|
target_dist = (1 - (target_support.unsqueeze(1) -
|
|
self.support.view(1, -1, 1)).abs() / self.delta_z
|
|
).clamp(0, 1) * next_dist.unsqueeze(1)
|
|
return target_dist.sum(-1)
|
|
|
|
def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
|
|
if self._target and self._iter % self._freq == 0:
|
|
self.sync_weight()
|
|
self.optim.zero_grad()
|
|
with torch.no_grad():
|
|
target_dist = self._target_dist(batch)
|
|
weight = batch.pop("weight", 1.0)
|
|
curr_dist = self(batch).logits
|
|
act = batch.act
|
|
curr_dist = curr_dist[np.arange(len(act)), act, :]
|
|
cross_entropy = - (target_dist * torch.log(curr_dist + 1e-8)).sum(1)
|
|
loss = (cross_entropy * weight).mean()
|
|
# ref: https://github.com/Kaixhin/Rainbow/blob/master/agent.py L94-100
|
|
batch.weight = cross_entropy.detach() # prio-buffer
|
|
loss.backward()
|
|
self.optim.step()
|
|
self._iter += 1
|
|
return {"loss": loss.item()}
|