def compute_returns()

in gala/storage.py [0:0]

• 40 lines of code
• 8 McCabe index (conditional complexity)

    def compute_returns(self,
                        next_value,
                        use_gae,
                        gamma,
                        gae_lambda,
                        use_proper_time_limits=True):
        if use_proper_time_limits:
            if use_gae:
                self.value_preds[-1] = next_value
                gae = 0
                for step in reversed(range(self.rewards.size(0))):
                    delta = self.rewards[step] + gamma * self.value_preds[
                        step + 1] * self.masks[step +
                                               1] - self.value_preds[step]
                    gae = delta + gamma * gae_lambda * self.masks[step +
                                                                  1] * gae
                    gae = gae * self.bad_masks[step + 1]
                    self.returns[step] = gae + self.value_preds[step]
            else:
                self.returns[-1] = next_value
                for step in reversed(range(self.rewards.size(0))):
                    self.returns[step] = (self.returns[step + 1] * \
                        gamma * self.masks[step + 1] + self.rewards[step]) * self.bad_masks[step + 1] \
                        + (1 - self.bad_masks[step + 1]) * self.value_preds[step]
        else:
            if use_gae:
                self.value_preds[-1] = next_value
                gae = 0
                for step in reversed(range(self.rewards.size(0))):
                    delta = self.rewards[step] + gamma * self.value_preds[
                        step + 1] * self.masks[step +
                                               1] - self.value_preds[step]
                    gae = delta + gamma * gae_lambda * self.masks[step +
                                                                  1] * gae
                    self.returns[step] = gae + self.value_preds[step]
            else:
                self.returns[-1] = next_value
                for step in reversed(range(self.rewards.size(0))):
                    self.returns[step] = self.returns[step + 1] * \
                        gamma * self.masks[step + 1] + self.rewards[step]