import torch
import numpy as np
from copy import deepcopy
import torch.nn.functional as F
from tianshou.data import Batch, PrioritizedReplayBuffer
from tianshou.policy import BasePolicy
[docs]class DQNPolicy(BasePolicy):
"""Implementation of Deep Q Network. arXiv:1312.5602
: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 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).
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
explanation.
"""
def __init__(self, model, optim, discount_factor=0.99,
estimation_step=1, target_update_freq=0, **kwargs):
super().__init__(**kwargs)
self.model = model
self.optim = optim
self.eps = 0
assert 0 <= discount_factor <= 1, 'discount_factor should in [0, 1]'
self._gamma = discount_factor
assert estimation_step > 0, 'estimation_step should greater than 0'
self._n_step = estimation_step
self._target = target_update_freq > 0
self._freq = target_update_freq
self._cnt = 0
if self._target:
self.model_old = deepcopy(self.model)
self.model_old.eval()
[docs] def set_eps(self, eps):
"""Set the eps for epsilon-greedy exploration."""
self.eps = eps
[docs] def train(self):
"""Set the module in training mode, except for the target network."""
self.training = True
self.model.train()
[docs] def eval(self):
"""Set the module in evaluation mode, except for the target network."""
self.training = False
self.model.eval()
[docs] def sync_weight(self):
"""Synchronize the weight for the target network."""
self.model_old.load_state_dict(self.model.state_dict())
[docs] def process_fn(self, batch, buffer, indice):
r"""Compute the n-step return for Q-learning targets:
.. math::
G_t = \sum_{i = t}^{t + n - 1} \gamma^{i - t}(1 - d_i)r_i +
\gamma^n (1 - d_{t + n}) \max_a Q_{old}(s_{t + n}, \arg\max_a
(Q_{new}(s_{t + n}, a)))
, where :math:`\gamma` is the discount factor,
:math:`\gamma \in [0, 1]`, :math:`d_t` is the done flag of step
:math:`t`. If there is no target network, the :math:`Q_{old}` is equal
to :math:`Q_{new}`.
"""
returns = np.zeros_like(indice)
gammas = np.zeros_like(indice) + self._n_step
for n in range(self._n_step - 1, -1, -1):
now = (indice + n) % len(buffer)
gammas[buffer.done[now] > 0] = n
returns[buffer.done[now] > 0] = 0
returns = buffer.rew[now] + self._gamma * returns
terminal = (indice + self._n_step - 1) % len(buffer)
terminal_data = buffer[terminal]
if self._target:
# target_Q = Q_old(s_, argmax(Q_new(s_, *)))
a = self(terminal_data, input='obs_next', eps=0).act
target_q = self(
terminal_data, model='model_old', input='obs_next').logits
if isinstance(target_q, torch.Tensor):
target_q = target_q.detach().cpu().numpy()
target_q = target_q[np.arange(len(a)), a]
else:
target_q = self(terminal_data, input='obs_next').logits
if isinstance(target_q, torch.Tensor):
target_q = target_q.detach().cpu().numpy()
target_q = target_q.max(axis=1)
target_q[gammas != self._n_step] = 0
returns += (self._gamma ** gammas) * target_q
batch.returns = returns
if isinstance(buffer, PrioritizedReplayBuffer):
q = self(batch).logits
q = q[np.arange(len(q)), batch.act]
r = batch.returns
if isinstance(r, np.ndarray):
r = torch.tensor(r, device=q.device, dtype=q.dtype)
td = r - q
buffer.update_weight(indice, td.detach().cpu().numpy())
impt_weight = torch.tensor(batch.impt_weight,
device=q.device, dtype=torch.float)
loss = (td.pow(2) * impt_weight).mean()
if not hasattr(batch, 'loss'):
batch.loss = loss
else:
batch.loss += loss
return batch
[docs] def forward(self, batch, state=None,
model='model', input='obs', eps=None, **kwargs):
"""Compute action over the given batch data.
:param float eps: in [0, 1], for epsilon-greedy exploration method.
:return: A :class:`~tianshou.data.Batch` which has 3 keys:
* ``act`` the action.
* ``logits`` the network's raw output.
* ``state`` the hidden state.
.. seealso::
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
more detailed explanation.
"""
model = getattr(self, model)
obs = getattr(batch, input)
q, h = model(obs, state=state, info=batch.info)
act = q.max(dim=1)[1].detach().cpu().numpy()
# add eps to act
if eps is None:
eps = self.eps
for i in range(len(q)):
if np.random.rand() < eps:
act[i] = np.random.randint(q.shape[1])
return Batch(logits=q, act=act, state=h)
[docs] def learn(self, batch, **kwargs):
if self._target and self._cnt % self._freq == 0:
self.sync_weight()
self.optim.zero_grad()
if hasattr(batch, 'loss'):
loss = batch.loss
else:
q = self(batch).logits
q = q[np.arange(len(q)), batch.act]
r = batch.returns
if isinstance(r, np.ndarray):
r = torch.tensor(r, device=q.device, dtype=q.dtype)
loss = F.mse_loss(q, r)
loss.backward()
self.optim.step()
self._cnt += 1
return {'loss': loss.item()}