There are two ways to run the environments

1. Providing actions for all agents at each time step:
   Environments cannot be rendered in real-time. This way is suited for training or sample collection.

import gym  
n_agents = [4]  
env = gym.make("gym_macm:cm-flock-v0", n_agents=n_agents)  
obs = env.obs  
while not env.done:  
    actions = {}  
    for agent in env.agents:  
        actions[agent.id] = myActor(obs[agent.id])  
    obs, rewards = env.step(actions)  

2. Providing actors for each agent at the creation of the environment:

from Box2D import b2Color  
n_agents = [4]  
actors = [myActor1()] * 2 + [myActor2()] * 2  
colors = [b2Color(0.6, 0.2, 0.6)] * 2 \  
         [b2Color(0.2, 0.7, 0.2)] * 2  
env = gym.make("gym_macm:cm-flock-v0",  
                n_agents = n_agents, actors = actors, colors = colors,  
                render = True, verbose_display = True)  
env.run()  

Soon: fixed and moving obstacles

Agents are expected to move to a randomly selected target location (If rendered, you can click to change the target location).

Rewards: In binary reward mode, agents are rewarded with +1 if they are closer than a predefined distance. In linear mode, rewards are inversely proportional with the distance between agents and their target. Agents are rewarded with -1 if they collide with another object.

Observation: Each agent observe relative locations of the target and closest of the remaining agents.

Multiple flocks: targets argument (optional) lets agents have different targets. E.g. gym.make(n_agents = 6, actors = [bots.flock()]*6, targets = [0,0,1,1,2,2]) creates 3 targets associated with 2 agents each.

Demos:
Three different Graph Neural Network models are trained with Double Q Learning algorithm and same hyperparameters.

$ cd <directory-to-clone>/gym-macm
$ pip install -e gym-macm