import torch
import numpy as np
from typing import Any, Dict, Union, Optional
from tianshou.policy import DQNPolicy
from tianshou.data import Batch, ReplayBuffer, to_numpy
[docs]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]
[docs] def forward(
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
model: str = "model",
input: str = "obs",
**kwargs: Any,
) -> Batch:
"""Compute action over the given batch data.
:return: A :class:`~tianshou.data.Batch` which has 2 keys:
* ``act`` the action.
* ``state`` the hidden state.
.. seealso::
Please refer to :meth:`~tianshou.policy.DQNPolicy.forward` for
more detailed explanation.
"""
model = getattr(self, model)
obs = batch[input]
obs_ = obs.obs if hasattr(obs, "obs") else obs
dist, h = model(obs_, state=state, info=batch.info)
q = (dist * self.support).sum(2)
act: np.ndarray = to_numpy(q.max(dim=1)[1])
if hasattr(obs, "mask"):
# some of actions are masked, they cannot be selected
q_: np.ndarray = to_numpy(q)
q_[~obs.mask] = -np.inf
act = q_.argmax(axis=1)
# add eps to act in training or testing phase
if not self.updating and not np.isclose(self.eps, 0.0):
for i in range(len(q)):
if np.random.rand() < self.eps:
q_ = np.random.rand(*q[i].shape)
if hasattr(obs, "mask"):
q_[~obs.mask[i]] = -np.inf
act[i] = q_.argmax()
return Batch(logits=dist, act=act, state=h)
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)
[docs] 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()}