stochastic_gaussian_mlp_policy.py 文件源码

def dist_info(self, obs, state_infos=None):
        if state_infos is None or len(state_infos) == 0:
            return self._f_dist_info(obs)
        if self._f_dist_info_givens is None:
            # compile function
            obs_var = self._mean_network.input_var
            latent_keys = ["latent_%d" % idx for idx in range(self._n_latent_layers)]
            latent_vars = [TT.matrix("latent_%d" % idx) for idx in range(self._n_latent_layers)]
            latent_dict = dict(list(zip(latent_keys, latent_vars)))
            self._f_dist_info_givens = ext.compile_function(
                inputs=[obs_var] + latent_vars,
                outputs=self.dist_info_sym(obs_var, latent_dict),
            )
        latent_vals = []
        for idx in range(self._n_latent_layers):
            latent_vals.append(state_infos["latent_%d" % idx])
        return self._f_dist_info_givens(*[obs] + latent_vals)