def __init__()

in distributed/rpc/batch/reinforce.py [0:0]

• 17 lines of code
• 6 McCabe index (conditional complexity)

    def __init__(self, world_size, batch=True):
        self.ob_rrefs = []
        self.agent_rref = RRef(self)
        self.rewards = {}
        self.policy = Policy(batch).cuda()
        self.optimizer = optim.Adam(self.policy.parameters(), lr=1e-2)
        self.running_reward = 0

        for ob_rank in range(1, world_size):
            ob_info = rpc.get_worker_info(OBSERVER_NAME.format(ob_rank))
            self.ob_rrefs.append(remote(ob_info, Observer, args=(batch,)))
            self.rewards[ob_info.id] = []

        self.states = torch.zeros(len(self.ob_rrefs), 1, 4)
        self.batch = batch
        # With batching, saved_log_probs contains a list of tensors, where each
        # tensor contains probs from all observers in one step.
        # Without batching, saved_log_probs is a dictionary where the key is the
        # observer id and the value is a list of probs for that observer.
        self.saved_log_probs = [] if self.batch else {k:[] for k in range(len(self.ob_rrefs))}
        self.future_actions = torch.futures.Future()
        self.lock = threading.Lock()
        self.pending_states = len(self.ob_rrefs)