def update()

in gala/gala_a2c.py [0:0]

• 25 lines of code
• 2 McCabe index (conditional complexity)

    def update(self, rollouts):
        obs_shape = rollouts.obs.size()[2:]
        action_shape = rollouts.actions.size()[-1]
        num_steps, num_processes, _ = rollouts.rewards.size()

        values, action_log_probs, dist_entropy, _ = self.actor_critic.evaluate_actions(
            rollouts.obs[:-1].view(-1, *obs_shape),
            rollouts.recurrent_hidden_states[0].view(
                -1, self.actor_critic.recurrent_hidden_state_size),
            rollouts.masks[:-1].view(-1, 1),
            rollouts.actions.view(-1, action_shape))

        values = values.view(num_steps, num_processes, 1)
        action_log_probs = action_log_probs.view(num_steps, num_processes, 1)

        advantages = rollouts.returns[:-1] - values
        value_loss = advantages.pow(2).mean()

        action_loss = -(advantages.detach() * action_log_probs).mean()

        self.optimizer.zero_grad()
        (value_loss * self.value_loss_coef + action_loss -
         dist_entropy * self.entropy_coef).backward()

        nn.utils.clip_grad_norm_(self.actor_critic.parameters(),
                                 self.max_grad_norm)

        self.optimizer.step()

        # Local-Gossip
        if self.gossip_buffer is not None:
            self.gossip_buffer.write_message(self.rank, self.actor_critic)
            self.gossip_buffer.aggregate_message(self.rank, self.actor_critic)

        return value_loss.item(), action_loss.item(), dist_entropy.item()