def agent_reward()

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

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

    def agent_reward(self, agent, world):
        # Agents are rewarded based on minimum agent distance to each landmark
        rew = 0
        shape = False
        adversaries = self.adversaries(world)
        if shape:
            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 -= 5
        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)  # 1 + (x - 1) * (x - 1)

        for p in range(world.dim_p):
            x = abs(agent.state.p_pos[p])
            rew -= 2 * bound(x)

        for food in world.food:
            if self.is_collision(agent, food):
                rew += 2
        rew += 0.05 * min([np.sqrt(np.sum(np.square(food.state.p_pos - agent.state.p_pos))) for food in world.food])

        return rew