def agent_reward()

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

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

    def agent_reward(self, agent, world):
        # Agents are negatively rewarded if caught by adversaries
        rew = 0
        shape = False
        adversaries = self.adversaries(world)
        if shape:  # reward can optionally be shaped (increased reward for increased distance from adversary)
            for adv in adversaries:
                rew += 0.1 * np.sqrt(np.sum(np.square(agent.state.p_pos - adv.state.p_pos)))
        if agent.collide:
            for a in adversaries:
                if self.is_collision(a, agent):
                    rew -= 10

        # agents are penalized for exiting the screen, so that they can be caught by the adversaries
        def bound(x):
            if x < 0.9:
                return 0
            if x < 1.0:
                return (x - 0.9) * 10
            return min(np.exp(2 * x - 2), 10)
        for p in range(world.dim_p):
            x = abs(agent.state.p_pos[p])
            rew -= bound(x)

        return rew