def _compute_path_probs(paths, pol_dist_type=None, insert=True,
                            insert_key='a_logprobs'):
        """
        Returns a N x T matrix of action probabilities
        """
        if pol_dist_type is None:
            # try to  infer distribution type
            path0 = paths[0]
            if 'log_std' in path0['agent_infos']:
                pol_dist_type = DIST_GAUSSIAN
            elif 'prob' in path0['agent_infos']:
                pol_dist_type = DIST_CATEGORICAL
            else:
                raise NotImplementedError()

        # compute path probs
        Npath = len(paths)
        actions = [path['actions'] for path in paths]
        if pol_dist_type == DIST_GAUSSIAN:
            params = [(path['agent_infos']['mean'], path['agent_infos']['log_std']) for path in paths]
            path_probs = [gauss_log_pdf(params[i], actions[i]) for i in range(Npath)]
        elif pol_dist_type == DIST_CATEGORICAL:
            params = [(path['agent_infos']['prob'],) for path in paths]
            path_probs = [categorical_log_pdf(params[i], actions[i]) for i in range(Npath)]
        else:
            raise NotImplementedError("Unknown distribution type")

        if insert:
            for i, path in enumerate(paths):
                path[insert_key] = path_probs[i]

        return np.array(path_probs)
    def _compute_path_probs(paths, pol_dist_type=None, insert=True,
                            insert_key='a_logprobs'):
        """
        Returns a N x T matrix of action probabilities
        """
        if pol_dist_type is None:
            # try to  infer distribution type
            path0 = paths[0]
            if 'log_std' in path0['agent_infos']:
                pol_dist_type = DIST_GAUSSIAN
            elif 'prob' in path0['agent_infos']:
                pol_dist_type = DIST_CATEGORICAL
            else:
                raise NotImplementedError()

        # compute path probs
        Npath = len(paths)
        actions = [path['actions'] for path in paths]
        if pol_dist_type == DIST_GAUSSIAN:
            params = [(path['agent_infos']['mean'], path['agent_infos']['log_std']) for path in paths]
            path_probs = [gauss_log_pdf(params[i], actions[i]) for i in range(Npath)]
        elif pol_dist_type == DIST_CATEGORICAL:
            params = [(path['agent_infos']['prob'],) for path in paths]
            path_probs = [categorical_log_pdf(params[i], actions[i]) for i in range(Npath)]
        else:
            raise NotImplementedError("Unknown distribution type")

        if insert:
            for i, path in enumerate(paths):
                path[insert_key] = path_probs[i]

        return np.array(path_probs)