读论文《IMPALA: Scalable Distributed DeepRL with Importance Weighted ActorLearner Architectures》——（续）实验部分

论文地址：

https://arxiv.org/pdf/1802.01561v2.pdf

 

 

 

论文《IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures》是基于论文《Safe and efficient off-policy reinforcement learning》改进后的分布式版本，基础论文《Safe and efficient off-policy reinforcement learning》的地址为：

https://arxiv.org/pdf/1606.02647.pdf

 

相关资料：

Deepmind Lab环境的python扩展库的安装：

https://www.cnblogs.com/devilmaycry812839668/p/16750126.html

 

读论文《IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures》

=========================================

官方的代码地址：（现已无法运行）

https://gitee.com/devilmaycry812839668/scalable_agent

需要注意的一点是这个offical的代码由于多年无人维护，现在已经无法运行，只做留档之用。

 

调试官方代码的相关资料：

出现`Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR`错误的解决办法

具体解法：

# 配置GPU参数
config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = 0.3

undefined symbol: _ZN10tensorflow7strings6StrCatERKNS0_8AlphaNumE

具体解法：

TF_INC="$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')"

TF_LIB="$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_lib())')"

g++ -std=c++11 -shared batcher.cc -o batcher.so -fPIC -I $TF_INC -O2 -D_GLIBCXX_USE_CXX11_ABI=1 -L$TF_LIB -ltensorflow_framework

=========================================

官方代码为python2.7版本，将部分代码升级为python3.6版本。

（https://gitee.com/devilmaycry812839668/scalable_agent）

dmlab30.py    修改为python3.6版本：

# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Utilities for DMLab-30."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections

import numpy as np
import tensorflow as tf


LEVEL_MAPPING = collections.OrderedDict([
    ('rooms_collect_good_objects_train', 'rooms_collect_good_objects_test'),
    ('rooms_exploit_deferred_effects_train',
     'rooms_exploit_deferred_effects_test'),
    ('rooms_select_nonmatching_object', 'rooms_select_nonmatching_object'),
    ('rooms_watermaze', 'rooms_watermaze'),
    ('rooms_keys_doors_puzzle', 'rooms_keys_doors_puzzle'),
    ('language_select_described_object', 'language_select_described_object'),
    ('language_select_located_object', 'language_select_located_object'),
    ('language_execute_random_task', 'language_execute_random_task'),
    ('language_answer_quantitative_question',
     'language_answer_quantitative_question'),
    ('lasertag_one_opponent_small', 'lasertag_one_opponent_small'),
    ('lasertag_three_opponents_small', 'lasertag_three_opponents_small'),
    ('lasertag_one_opponent_large', 'lasertag_one_opponent_large'),
    ('lasertag_three_opponents_large', 'lasertag_three_opponents_large'),
    ('natlab_fixed_large_map', 'natlab_fixed_large_map'),
    ('natlab_varying_map_regrowth', 'natlab_varying_map_regrowth'),
    ('natlab_varying_map_randomized', 'natlab_varying_map_randomized'),
    ('skymaze_irreversible_path_hard', 'skymaze_irreversible_path_hard'),
    ('skymaze_irreversible_path_varied', 'skymaze_irreversible_path_varied'),
    ('psychlab_arbitrary_visuomotor_mapping',
     'psychlab_arbitrary_visuomotor_mapping'),
    ('psychlab_continuous_recognition', 'psychlab_continuous_recognition'),
    ('psychlab_sequential_comparison', 'psychlab_sequential_comparison'),
    ('psychlab_visual_search', 'psychlab_visual_search'),
    ('explore_object_locations_small', 'explore_object_locations_small'),
    ('explore_object_locations_large', 'explore_object_locations_large'),
    ('explore_obstructed_goals_small', 'explore_obstructed_goals_small'),
    ('explore_obstructed_goals_large', 'explore_obstructed_goals_large'),
    ('explore_goal_locations_small', 'explore_goal_locations_small'),
    ('explore_goal_locations_large', 'explore_goal_locations_large'),
    ('explore_object_rewards_few', 'explore_object_rewards_few'),
    ('explore_object_rewards_many', 'explore_object_rewards_many'),
])

HUMAN_SCORES = {
    'rooms_collect_good_objects_test': 10,
    'rooms_exploit_deferred_effects_test': 85.65,
    'rooms_select_nonmatching_object': 65.9,
    'rooms_watermaze': 54,
    'rooms_keys_doors_puzzle': 53.8,
    'language_select_described_object': 389.5,
    'language_select_located_object': 280.7,
    'language_execute_random_task': 254.05,
    'language_answer_quantitative_question': 184.5,
    'lasertag_one_opponent_small': 12.65,
    'lasertag_three_opponents_small': 18.55,
    'lasertag_one_opponent_large': 18.6,
    'lasertag_three_opponents_large': 31.5,
    'natlab_fixed_large_map': 36.9,
    'natlab_varying_map_regrowth': 24.45,
    'natlab_varying_map_randomized': 42.35,
    'skymaze_irreversible_path_hard': 100,
    'skymaze_irreversible_path_varied': 100,
    'psychlab_arbitrary_visuomotor_mapping': 58.75,
    'psychlab_continuous_recognition': 58.3,
    'psychlab_sequential_comparison': 39.5,
    'psychlab_visual_search': 78.5,
    'explore_object_locations_small': 74.45,
    'explore_object_locations_large': 65.65,
    'explore_obstructed_goals_small': 206,
    'explore_obstructed_goals_large': 119.5,
    'explore_goal_locations_small': 267.5,
    'explore_goal_locations_large': 194.5,
    'explore_object_rewards_few': 77.7,
    'explore_object_rewards_many': 106.7,
}

RANDOM_SCORES = {
    'rooms_collect_good_objects_test': 0.073,
    'rooms_exploit_deferred_effects_test': 8.501,
    'rooms_select_nonmatching_object': 0.312,
    'rooms_watermaze': 4.065,
    'rooms_keys_doors_puzzle': 4.135,
    'language_select_described_object': -0.07,
    'language_select_located_object': 1.929,
    'language_execute_random_task': -5.913,
    'language_answer_quantitative_question': -0.33,
    'lasertag_one_opponent_small': -0.224,
    'lasertag_three_opponents_small': -0.214,
    'lasertag_one_opponent_large': -0.083,
    'lasertag_three_opponents_large': -0.102,
    'natlab_fixed_large_map': 2.173,
    'natlab_varying_map_regrowth': 2.989,
    'natlab_varying_map_randomized': 7.346,
    'skymaze_irreversible_path_hard': 0.1,
    'skymaze_irreversible_path_varied': 14.4,
    'psychlab_arbitrary_visuomotor_mapping': 0.163,
    'psychlab_continuous_recognition': 0.224,
    'psychlab_sequential_comparison': 0.129,
    'psychlab_visual_search': 0.085,
    'explore_object_locations_small': 3.575,
    'explore_object_locations_large': 4.673,
    'explore_obstructed_goals_small': 6.76,
    'explore_obstructed_goals_large': 2.61,
    'explore_goal_locations_small': 7.66,
    'explore_goal_locations_large': 3.14,
    'explore_object_rewards_few': 2.073,
    'explore_object_rewards_many': 2.438,
}

ALL_LEVELS = frozenset([
    'rooms_collect_good_objects_train',
    'rooms_collect_good_objects_test',
    'rooms_exploit_deferred_effects_train',
    'rooms_exploit_deferred_effects_test',
    'rooms_select_nonmatching_object',
    'rooms_watermaze',
    'rooms_keys_doors_puzzle',
    'language_select_described_object',
    'language_select_located_object',
    'language_execute_random_task',
    'language_answer_quantitative_question',
    'lasertag_one_opponent_small',
    'lasertag_three_opponents_small',
    'lasertag_one_opponent_large',
    'lasertag_three_opponents_large',
    'natlab_fixed_large_map',
    'natlab_varying_map_regrowth',
    'natlab_varying_map_randomized',
    'skymaze_irreversible_path_hard',
    'skymaze_irreversible_path_varied',
    'psychlab_arbitrary_visuomotor_mapping',
    'psychlab_continuous_recognition',
    'psychlab_sequential_comparison',
    'psychlab_visual_search',
    'explore_object_locations_small',
    'explore_object_locations_large',
    'explore_obstructed_goals_small',
    'explore_obstructed_goals_large',
    'explore_goal_locations_small',
    'explore_goal_locations_large',
    'explore_object_rewards_few',
    'explore_object_rewards_many',
])


def _transform_level_returns(level_returns):
  """Converts training level names to test level names."""
  new_level_returns = {}
  for level_name, returns in level_returns.items():
    new_level_returns[LEVEL_MAPPING.get(level_name, level_name)] = returns

  test_set = set(LEVEL_MAPPING.values())
  diff = test_set - set(new_level_returns.keys())
  if diff:
    raise ValueError('Missing levels: %s' % list(diff))

  for level_name, returns in new_level_returns.items():
    if level_name in test_set:
      if not returns:
        raise ValueError('Missing returns for level: \'%s\': ' % level_name)
    else:
      tf.logging.info('Skipping level %s for calculation.', level_name)

  return new_level_returns


def compute_human_normalized_score(level_returns, per_level_cap):
  """Computes human normalized score.

  Levels that have different training and test versions, will use the returns
  for the training level to calculate the score. E.g.
  'rooms_collect_good_objects_train' will be used for
  'rooms_collect_good_objects_test'. All returns for levels not in DmLab-30
  will be ignored.

  Args:
    level_returns: A dictionary from level to list of episode returns.
    per_level_cap: A percentage cap (e.g. 100.) on the per level human
      normalized score. If None, no cap is applied.

  Returns:
    A float with the human normalized score in percentage.

  Raises:
    ValueError: If a level is missing from `level_returns` or has no returns.
  """
  new_level_returns = _transform_level_returns(level_returns)

  def human_normalized_score(level_name, returns):
    score = np.mean(returns)
    human = HUMAN_SCORES[level_name]
    random = RANDOM_SCORES[level_name]
    human_normalized_score = (score - random) / (human - random) * 100
    if per_level_cap is not None:
      human_normalized_score = min(human_normalized_score, per_level_cap)
    return human_normalized_score

  return np.mean(
      [human_normalized_score(k, v) for k, v in new_level_returns.items()])

py_process.py   修改为python3.6版本：

# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""PyProcess.

This file includes utilities for running code in separate Python processes as
part of a TensorFlow graph. It is similar to tf.py_func, but the code is run in
separate processes to avoid the GIL.

Example:

  class Zeros(object):

    def __init__(self, dim0):
      self._dim0 = dim0

    def compute(self, dim1):
      return np.zeros([self._dim0, dim1], dtype=np.int32)

    @staticmethod
    def _tensor_specs(method_name, kwargs, constructor_kwargs):
      dim0 = constructor_kwargs['dim0']
      dim1 = kwargs['dim1']
      if method_name == 'compute':
        return tf.contrib.framework.TensorSpec([dim0, dim1], tf.int32)

  with tf.Graph().as_default():
    p = py_process.PyProcess(Zeros, 1)
    result = p.proxy.compute(2)

    with tf.train.SingularMonitoredSession(
        hooks=[py_process.PyProcessHook()]) as session:
      print(session.run(result))  # Prints [[0, 0]].
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import multiprocessing

import tensorflow as tf

from tensorflow.python.util import function_utils


nest = tf.contrib.framework.nest


class _TFProxy(object):
  """A proxy that creates TensorFlow operations for each method call to a
  separate process."""

  def __init__(self, type_, constructor_kwargs):
    self._type = type_
    self._constructor_kwargs = constructor_kwargs

  def __getattr__(self, name):
    def call(*args):
      kwargs = dict(
          zip(function_utils.fn_args(getattr(self._type, name))[1:], args))
      specs = self._type._tensor_specs(name, kwargs, self._constructor_kwargs)

      if specs is None:
        raise ValueError(
            'No tensor specifications were provided for: %s' % name)

      flat_dtypes = nest.flatten(nest.map_structure(lambda s: s.dtype, specs))
      flat_shapes = nest.flatten(nest.map_structure(lambda s: s.shape, specs))

      def py_call(*args):
        try:
          self._out.send(args)
          result = self._out.recv()
          if isinstance(result, Exception):
            raise result
          if result is not None:
            return result
        except Exception as e:
          if isinstance(e, IOError):
            raise StopIteration()  # Clean exit.
          else:
            raise

      result = tf.py_func(py_call, (name,) + tuple(args), flat_dtypes,
                          name=name)

      if isinstance(result, tf.Operation):
        return result

      for t, shape in zip(result, flat_shapes):
        t.set_shape(shape)
      return nest.pack_sequence_as(specs, result)
    return call

  def _start(self):
    self._out, in_ = multiprocessing.Pipe()
    self._process = multiprocessing.Process(
        target=self._worker_fn,
        args=(self._type, self._constructor_kwargs, in_))
    self._process.start()
    result = self._out.recv()

    if isinstance(result, Exception):
      raise result

  def _close(self, session):
    try:
      self._out.send(None)
      self._out.close()
    except IOError:
      pass
    self._process.join()

  def _worker_fn(self, type_, constructor_kwargs, in_):
    try:
      o = type_(**constructor_kwargs)

      in_.send(None)  # Ready.

      while True:
        # Receive request.
        serialized = in_.recv()

        if serialized is None:
          if hasattr(o, 'close'):
            o.close()
          in_.close()
          return

        # method_name = str(serialized[0])
        method_name = serialized[0].decode()
        inputs = serialized[1:]

        # Compute result.
        results = getattr(o, method_name)(*inputs)
        if results is not None:
          results = nest.flatten(results)

        # Respond.
        in_.send(results)
    except Exception as e:
      if 'o' in locals() and hasattr(o, 'close'):
        try:
          o.close()
        except:
          pass
      in_.send(e)


class PyProcess(object):
  COLLECTION = 'py_process_processes'

  def __init__(self, type_, *constructor_args, **constructor_kwargs):
    self._type = type_
    self._constructor_kwargs = dict(
        zip(function_utils.fn_args(type_.__init__)[1:], constructor_args))
    self._constructor_kwargs.update(constructor_kwargs)

    tf.add_to_collection(PyProcess.COLLECTION, self)

    self._proxy = _TFProxy(type_, self._constructor_kwargs)

  @property
  def proxy(self):
    """A proxy that creates TensorFlow operations for each method call."""
    return self._proxy

  def close(self, session):
    self._proxy._close(session)

  def start(self):
    self._proxy._start()


class PyProcessHook(tf.train.SessionRunHook):
  """A MonitoredSession hook that starts and stops PyProcess instances."""

  def begin(self):
    tf.logging.info('Starting all processes.')
    tp = multiprocessing.pool.ThreadPool()
    tp.map(lambda p: p.start(), tf.get_collection(PyProcess.COLLECTION))
    tp.close()
    tp.join()
    tf.logging.info('All processes started.')

  def end(self, session):
    tf.logging.info('Closing all processes.')
    tp = multiprocessing.pool.ThreadPool()
    tp.map(lambda p: p.close(session), tf.get_collection(PyProcess.COLLECTION))
    tp.close()
    tp.join()
    tf.logging.info('All processes closed.')

对 experiment.py 文件进行修改，具体修改不给出了，已经将所有修改后的代码上传到：

 https://gitee.com/devilmaycry812839668/scalable_agent

 

=====================================================

经过以上修改和配置后，官方代码可以在单机模式下正常运行，唯一的问题就是太消耗资源了，具体命令：

python experiment.py --num_actors=1 --batch_size=1

运行：

------------------------------------------------

2022年10月29日更新

单机使用12个actor运行，硬件为cpu 10700k，gpu为rtx2070super，运行命令：

平均帧率：4000

单个实验设定的帧数为10**9，那么一个环境的实验在个人主机上运行大概需要时间：70个小时，3天时间

--------------------------

这个时间消耗十分的巨大，本打算能成功跑完一个环境，不过现在看看也是不现实的。

============================================