def learn(self, batch: Batch, **kwargs) -> Dict[str, float]:
if self._target and self._cnt % self._freq == 0:
self.sync_weight()
self.optim.zero_grad()
q = self(batch).logits
q = q[np.arange(len(q)), batch.act]
r = to_torch_as(batch.returns, q)
if hasattr(batch, 'update_weight'):
td = r - q
batch.update_weight(batch.indice, to_numpy(td))
impt_weight = to_torch_as(batch.impt_weight, q)
loss = (td.pow(2) * impt_weight).mean()
else:
loss = F.mse_loss(q, r)
loss.backward()
self.optim.step()
self._cnt += 1
return {'loss': loss.item()}
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}`.
"""
batch = self.compute_nstep_return(batch, buffer, indice,
self._target_q, self._gamma,
self._n_step)
if isinstance(buffer, PrioritizedReplayBuffer):
batch.update_weight = buffer.update_weight
batch.indice = indice
return batch
