def __init__()

in multiagent/environment.py [0:0]

• 49 lines of code
• 16 McCabe index (conditional complexity)

    def __init__(self, world, reset_callback=None, reward_callback=None,
                 observation_callback=None, info_callback=None,
                 done_callback=None, shared_viewer=True):

        self.world = world
        self.agents = self.world.policy_agents
        # set required vectorized gym env property
        self.n = len(world.policy_agents)
        # scenario callbacks
        self.reset_callback = reset_callback
        self.reward_callback = reward_callback
        self.observation_callback = observation_callback
        self.info_callback = info_callback
        self.done_callback = done_callback
        # environment parameters
        self.discrete_action_space = True
        # if true, action is a number 0...N, otherwise action is a one-hot N-dimensional vector
        self.discrete_action_input = False
        # if true, even the action is continuous, action will be performed discretely
        self.force_discrete_action = world.discrete_action if hasattr(world, 'discrete_action') else False
        # if true, every agent has the same reward
        self.shared_reward = world.collaborative if hasattr(world, 'collaborative') else False
        self.time = 0

        # configure spaces
        self.action_space = []
        self.observation_space = []
        for agent in self.agents:
            total_action_space = []
            # physical action space
            if self.discrete_action_space:
                u_action_space = spaces.Discrete(world.dim_p * 2 + 1)
            else:
                u_action_space = spaces.Box(low=-agent.u_range, high=+agent.u_range, shape=(world.dim_p,), dtype=np.float32)
            if agent.movable:
                total_action_space.append(u_action_space)
            # communication action space
            if self.discrete_action_space:
                c_action_space = spaces.Discrete(world.dim_c)
            else:
                c_action_space = spaces.Box(low=0.0, high=1.0, shape=(world.dim_c,), dtype=np.float32)
            if not agent.silent:
                total_action_space.append(c_action_space)
            # total action space
            if len(total_action_space) > 1:
                # all action spaces are discrete, so simplify to MultiDiscrete action space
                if all([isinstance(act_space, spaces.Discrete) for act_space in total_action_space]):
                    act_space = MultiDiscrete([[0, act_space.n - 1] for act_space in total_action_space])
                else:
                    act_space = spaces.Tuple(total_action_space)
                self.action_space.append(act_space)
            else:
                self.action_space.append(total_action_space[0])
            # observation space
            obs_dim = len(observation_callback(agent, self.world))
            self.observation_space.append(spaces.Box(low=-np.inf, high=+np.inf, shape=(obs_dim,), dtype=np.float32))
            agent.action.c = np.zeros(self.world.dim_c)

        # rendering
        self.shared_viewer = shared_viewer
        if self.shared_viewer:
            self.viewers = [None]
        else:
            self.viewers = [None] * self.n
        self._reset_render()