def _sample_new_players()

in randomized_uncertain_social_preferences/rusp/env_indirect_reciprocity.py [0:0]

• 26 lines of code
• 12 McCabe index (conditional complexity)

    def _sample_new_players(self):
        exclude_first = self.last_step_first_agent_vs_last_agent and self._t < self._horizon - 1
        must_include_first_last = (self.last_step_first_agent_vs_last_agent and self._t == self._horizon - 1)
        exclude_last = (self.last_doesnt_play_until_t is not None and self._t < self.last_doesnt_play_until_t)

        p1_options = np.arange(self.n_agents)
        p2_options = np.arange(self.n_agents)

        if self.last_agent_always_plays and not exclude_last:
            p2_options = np.array([self.n_agents - 1])
            p1_options = p1_options[p1_options != p2_options]
        if exclude_last:
            p1_options = p1_options[p1_options != self.n_agents - 1]
            p2_options = p2_options[p2_options != self.n_agents - 1]
        if must_include_first_last:
            p1_options = np.array([0])
            p2_options = np.array([self.n_agents - 1])
        if exclude_first:
            p1_options = p1_options[p1_options != 0]
            p2_options = p2_options[p2_options != 0]
        if self.last_doesnt_play_until_t is not None and self.last_doesnt_play_until_t == self._t and self.last_must_play_at_t:
            p1_options = p1_options[p1_options != self.n_agents - 1]
            p2_options = np.array([self.n_agents - 1])

        p1 = np.random.choice(p1_options)
        p2_options = p2_options[p2_options != p1]
        p2 = np.random.choice(p2_options)

        self._youre_playing = np.zeros((self.n_agents,), dtype=bool)
        self._youre_playing[[p1, p2]] = 1