Tf-Agents ParallelPyEnvironment静默失败

Question

我已经编写了一个自定义环境，因此我可以玩增强学习（PPO）和TF代理。 如果我将env（继承自py_environment.PyEnvironment）包装在TfPyEnvironment ，则此方法TfPyEnvironment ，但是如果我尝试将其包装到ParallelPyEnvironment ，则失败。 我尝试使用ParallelPyEnvironment所有关键字参数，但是代码只运行到该行，然后什么也没发生-没有异常，该程序不会终止，等等。

这是我的代码初始化环境并展示eval_env的工作变体：

train_env = tf_py_environment.TFPyEnvironment(
    ParallelPyEnvironment(
        [CardGameEnv()] * hparams['parallel_environments']
    )
)
# this works perfectly:
eval_env = tf_py_environment.TFPyEnvironment(CardGameEnv(debug=True))

如果我通过CTRL+C终止脚本，则正在输出：

Traceback (most recent call last):
Traceback (most recent call last):
  File "E:\Users\tmp\Documents\Programming\Neural Nets\Poker_AI\poker_logic\train.py", line 229, in <module>
  File "<string>", line 1, in <module>
    train(model_num=3)
  File "C:\Python37\lib\multiprocessing\spawn.py", line 105, in spawn_main
  File "E:\Users\tmp\Documents\Programming\Neural Nets\Poker_AI\poker_logic\train.py", line 64, in train
    [CardGameEnv()] * hparams['parallel_environments']
    exitcode = _main(fd)
  File "E:\Users\tmp\AppData\Roaming\Python\Python37\site-packages\gin\config.py", line 1009, in wrapper
  File "C:\Python37\lib\multiprocessing\spawn.py", line 113, in _main
    preparation_data = reduction.pickle.load(from_parent)
KeyboardInterrupt
    return fn(*new_args, **new_kwargs)
  File "C:\Python37\lib\site-packages\tf_agents\environments\parallel_py_environment.py", line 70, in __init__
    self.start()
  File "C:\Python37\lib\site-packages\tf_agents\environments\parallel_py_environment.py", line 83, in start
    env.start(wait_to_start=self._start_serially)
  File "C:\Python37\lib\site-packages\tf_agents\environments\parallel_py_environment.py", line 223, in start
    self._process.start()
  File "C:\Python37\lib\multiprocessing\process.py", line 112, in start
    self._popen = self._Popen(self)
  File "C:\Python37\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\Python37\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "C:\Python37\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
    reduction.dump(process_obj, to_child)
  File "C:\Python37\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
  File "C:\Python37\lib\site-packages\tf_agents\environments\parallel_py_environment.py", line 264, in __getattr__
    return self._receive()
  File "C:\Python37\lib\site-packages\tf_agents\environments\parallel_py_environment.py", line 333, in _receive
    message, payload = self._conn.recv()
  File "C:\Python37\lib\multiprocessing\connection.py", line 250, in recv
    buf = self._recv_bytes()
  File "C:\Python37\lib\multiprocessing\connection.py", line 306, in _recv_bytes
    [ov.event], False, INFINITE)
KeyboardInterrupt
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
  File "C:\Python37\lib\site-packages\tf_agents\environments\parallel_py_environment.py", line 289, in close
    self._process.join(5)
  File "C:\Python37\lib\multiprocessing\process.py", line 139, in join
    assert self._popen is not None, 'can only join a started process'
AssertionError: can only join a started process

由此得出的结论是， ParallelPyEnvironment正在尝试启动的线程并不能做到这一点，但是由于我对Python线程的使用经验不是很丰富，所以我不知道从这里开始应该走什么路，特别是如何解决这个问题。 当前的培训需要很长时间，并且根本没有使用我的PC的功能（使用了3GB的32GB RAM，处理器使用了3％，GPU几乎无法工作，但VRAM已满），因此这将大大缩短培训时间。

Answer 1

解决方案是传递可调用对象，而不是环境，因此ParallelPyEnvironment可以自己构造它们：

train_env = tf_py_environment.TFPyEnvironment(
    ParallelPyEnvironment(
        [CardGameEnv] * hparams['parallel_environments']
    )
)