def _backward(self, batch):
"""Loss is encoded in here. Defining a new loss function
would start by rewriting this function"""
states, acs, advs, rs, _ = convert_batch(batch)
values, ac_logprobs, entropy = self._evaluate(states, acs)
pi_err = -(advs * ac_logprobs).sum()
value_err = 0.5 * (values - rs).pow(2).sum()
self.optimizer.zero_grad()
overall_err = 0.5 * value_err + pi_err - entropy * 0.01
overall_err.backward()
torch.nn.utils.clip_grad_norm(self._model.parameters(), 40)
def _backward(self, batch):
"""Loss is encoded in here. Defining a new loss function
would start by rewriting this function"""
states, acs, advs, rs, _ = convert_batch(batch)
values, ac_logprobs, entropy = self._evaluate(states, acs)
pi_err = -(advs * ac_logprobs).sum()
value_err = 0.5 * (values - rs).pow(2).sum()
self.optimizer.zero_grad()
overall_err = (pi_err +
value_err * self.config["vf_loss_coeff"] +
entropy * self.config["entropy_coeff"])
overall_err.backward()
torch.nn.utils.clip_grad_norm(
self._model.parameters(), self.config["grad_clip"])
def _backward(self, batch):
"""Loss is encoded in here. Defining a new loss function
would start by rewriting this function"""
states, actions, advs, rs, _ = convert_batch(batch)
values, action_log_probs, entropy = self._evaluate(states, actions)
pi_err = -advs.dot(action_log_probs.reshape(-1))
value_err = F.mse_loss(values.reshape(-1), rs)
self.optimizer.zero_grad()
overall_err = sum([
pi_err,
self.config["vf_loss_coeff"] * value_err,
self.config["entropy_coeff"] * entropy,
])
overall_err.backward()
torch.nn.utils.clip_grad_norm_(self._model.parameters(),
self.config["grad_clip"])
