def reset_world()

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

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

    def reset_world(self, world):
        # random properties for agents
        for i, agent in enumerate(world.agents):
            agent.color = np.array([0.45, 0.95, 0.45]) if not agent.adversary else np.array([0.95, 0.45, 0.45])
            agent.color -= np.array([0.3, 0.3, 0.3]) if agent.leader else np.array([0, 0, 0])
            # random properties for landmarks
        for i, landmark in enumerate(world.landmarks):
            landmark.color = np.array([0.25, 0.25, 0.25])
        for i, landmark in enumerate(world.food):
            landmark.color = np.array([0.15, 0.15, 0.65])
        for i, landmark in enumerate(world.forests):
            landmark.color = np.array([0.6, 0.9, 0.6])
        # set random initial states
        for agent in world.agents:
            agent.state.p_pos = np.random.uniform(-1, +1, world.dim_p)
            agent.state.p_vel = np.zeros(world.dim_p)
            agent.state.c = np.zeros(world.dim_c)
        for i, landmark in enumerate(world.landmarks):
            landmark.state.p_pos = np.random.uniform(-0.9, +0.9, world.dim_p)
            landmark.state.p_vel = np.zeros(world.dim_p)
        for i, landmark in enumerate(world.food):
            landmark.state.p_pos = np.random.uniform(-0.9, +0.9, world.dim_p)
            landmark.state.p_vel = np.zeros(world.dim_p)
        for i, landmark in enumerate(world.forests):
            landmark.state.p_pos = np.random.uniform(-0.9, +0.9, world.dim_p)
            landmark.state.p_vel = np.zeros(world.dim_p)