def post_process_fn(self, batch: Batch,
buffer: ReplayBuffer, indice: np.ndarray) -> None:
"""Post-process the data from the provided replay buffer. Typical
usage is to update the sampling weight in prioritized experience
replay. Check out :ref:`policy_concept` for more information.
"""
if isinstance(buffer, PrioritizedReplayBuffer) \
and hasattr(batch, 'weight'):
buffer.update_weight(indice, batch.weight)
def post_process_fn(self, batch: Batch, buffer: ReplayBuffer,
indice: np.ndarray) -> None:
"""Post-process the data from the provided replay buffer.
Typical usage is to update the sampling weight in prioritized
experience replay. Used in :meth:`update`.
"""
if hasattr(buffer, "update_weight") and hasattr(batch, "weight"):
buffer.update_weight(indice, batch.weight)
def process_fn(self, batch: Batch, buffer: ReplayBuffer,
indice: np.ndarray) -> Batch:
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
