def agent_reward()

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

• 23 lines of code
• 13 McCabe index (conditional complexity)

    def agent_reward(self, agent, world):
        # Rewarded based on how close any good agent is to the goal landmark, and how far the adversary is from it
        shaped_reward = True
        shaped_adv_reward = True

        # Calculate negative reward for adversary
        adversary_agents = self.adversaries(world)
        if shaped_adv_reward:  # distance-based adversary reward
            adv_rew = sum([np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos))) for a in adversary_agents])
        else:  # proximity-based adversary reward (binary)
            adv_rew = 0
            for a in adversary_agents:
                if np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos))) < 2 * a.goal_a.size:
                    adv_rew -= 5

        # Calculate positive reward for agents
        good_agents = self.good_agents(world)
        if shaped_reward:  # distance-based agent reward
            pos_rew = -min(
                [np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos))) for a in good_agents])
        else:  # proximity-based agent reward (binary)
            pos_rew = 0
            if min([np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos))) for a in good_agents]) \
                    < 2 * agent.goal_a.size:
                pos_rew += 5
            pos_rew -= min(
                [np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos))) for a in good_agents])
        return pos_rew + adv_rew