latent_regressor.py 文件源码

def get_output_p(self, path):  # this gives the p_dist for every step: the latent posterior wrt obs_act
        if self.recurrent:
            obs_actions = [np.concatenate([path["observations"][:, self.obs_regressed],
                                           path["actions"][:, self.act_regressed]],
                                          axis=1)]  # is this the same??
        else:
            obs_actions = np.concatenate([path["observations"][:, self.obs_regressed],
                                          path["actions"][:, self.act_regressed]], axis=1)
        if self.noisify_traj_coef:
            obs_actions += np.random.normal(loc=0.0, scale=float(np.mean(np.abs(obs_actions))) * self.noisify_traj_coef,
                                            size=np.shape(obs_actions))
        if self.use_only_sign:
            obs_actions = np.sign(obs_actions)
        if self.policy.latent_name == 'bernoulli':
            return self._regressor._f_p(obs_actions).flatten()
        elif self.policy.latent_name == 'normal':
            return self._regressor._f_pdists(obs_actions).flatten()