def reset_world()

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

• 22 lines of code
• 8 McCabe index (conditional complexity)

    def reset_world(self, world):
        # assign goals to agents
        for agent in world.agents:
            agent.goal_a = None
            agent.goal_b = None
        # want other agent to go to the goal landmark
        world.agents[0].goal_a = world.agents[1]
        world.agents[0].goal_b = np.random.choice(world.landmarks)
        world.agents[1].goal_a = world.agents[0]
        world.agents[1].goal_b = np.random.choice(world.landmarks)
        # random properties for agents
        for i, agent in enumerate(world.agents):
            agent.color = np.array([0.25,0.25,0.25])               
        # random properties for landmarks
        world.landmarks[0].color = np.array([0.75,0.25,0.25]) 
        world.landmarks[1].color = np.array([0.25,0.75,0.25]) 
        world.landmarks[2].color = np.array([0.25,0.25,0.75]) 
        # special colors for goals
        world.agents[0].goal_a.color = world.agents[0].goal_b.color                
        world.agents[1].goal_a.color = world.agents[1].goal_b.color                               
        # 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(-1,+1, world.dim_p)
            landmark.state.p_vel = np.zeros(world.dim_p)