def adversary_reward()

in multiagent/scenarios/simple_tag.py [0:0]

• 14 lines of code
• 10 McCabe index (conditional complexity)

    def adversary_reward(self, agent, world):
        # Adversaries are rewarded for collisions with agents
        rew = 0
        shape = False
        agents = self.good_agents(world)
        adversaries = self.adversaries(world)
        if shape:  # reward can optionally be shaped (decreased reward for increased distance from agents)
            for adv in adversaries:
                rew -= 0.1 * min([np.sqrt(np.sum(np.square(a.state.p_pos - adv.state.p_pos))) for a in agents])
        if agent.collide:
            for ag in agents:
                for adv in adversaries:
                    if self.is_collision(ag, adv):
                        rew += 10
        return rew