def __init__(self):
self.show_ui = options.get('show_ui', False)
self.lab_path = options.get('environment/lab_path', '/lab')
self.level_script = options.get('environment/level_script', None)
if self.level_script is None:
self.level_script = ''
else:
self.level_script = ' --level_script %s' % self.level_script
def __init__(self):
self.exploit = options.get('exploit', False)
self.max_episodes = options.get('environment/max_episodes', 1)
self.infinite_run = options.get('environment/infinite_run', False)
rlx_address = options.get('rlx_server_address', None)
if rlx_address is None:
rlx_address = options.get('relaax_rlx_server/bind',
'localhost:7001')
self.agent = AgentProxy(rlx_address)
def __init__(self, env='CartPole-v0'):
self.gym = gym.make(env)
frame_skip = options.get('environment/frame_skip', None)
if frame_skip is not None:
skip_wrapper = SkipWrapper(frame_skip)
self.gym = skip_wrapper(self.gym)
self._record = options.get('environment/record', False)
if self._record:
out_dir = options.get('environment/out_dir', '/tmp/' + env)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
self.gym = gym.wrappers.Monitor(self.gym, out_dir, force=True)
self._no_op_max = options.get('environment/no_op_max', 0)
self._reset_action = self.gym.action_space.sample() \
if options.get('environment/stochastic_reset', False) else 0
self.gym.seed(random.randrange(1000000))
self._show_ui = options.get('show_ui', False)
limit = options.get(
'environment/limit',
self.gym.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps'))
if limit is not None:
self.gym._max_episode_steps = limit
shape = options.get('environment/shape',
options.get('environment/image', (84, 84)))
self._shape = shape[:2]
if len(self._shape) > 1:
self._channels = 0 if len(shape) == 2 else shape[-1]
self._crop = options.get('environment/crop', True)
self._process_state = self._process_all
atari = [name + 'Deterministic'
for name in GymEnv.AtariGameList] + GymEnv.AtariGameList
if any(item.startswith(env.split('-')[0]) for item in atari):
self._process_state = self._process_img
self.action_size = self._get_action_size()
if self.action_size != options.algorithm.output.action_size:
log.error(
'Algorithm expects action size %d; gym return %d. \n'
'Please set correct action size in you configuration yaml.' %
(options.algorithm.output.action_size, self.action_size))
sys.exit(-1)
self._scale = (1.0 / 255.0)
self.reset()
def __init__(self):
super(Training, self).__init__()
self.steps = options.get('environment/steps', 1000)
self.history_len = 2
self.ring_buffer = RingBuffer(self.history_len)
for _ in range(self.ring_buffer.size - 1):
self.ring_buffer.append(0)
def __init__(self, level='ppaquette/DoomMyWayHome-v0'):
time.sleep(np.random.randint(100))
env = gym.make(level)
modewrapper = wrappers.SetPlayingMode('algo')
obwrapper = wrappers.SetResolution('160x120')
acwrapper = wrappers.ToDiscrete('minimal')
env = modewrapper(obwrapper(acwrapper(env)))
frame_skip = options.get('environment/frame_skip', None)
if frame_skip is not None:
skip_wrapper = SkipWrapper(frame_skip)
env = skip_wrapper(env)
self._record = options.get('environment/record', False)
if self._record:
out_dir = options.get('environment/out_dir',
'/tmp/' + level.split('/')[-1])
if not os.path.exists(out_dir):
os.makedirs(out_dir)
env = gym.wrappers.Monitor(env, out_dir, force=True)
self._no_op_max = options.get('environment/no_op_max', 0)
self._reset_action = env.action_space.sample() \
if options.get('environment/stochastic_reset', False) else 0
env.seed(random.randrange(1000000))
self._show_ui = options.get('show_ui', False)
limit = options.get(
'environment/limit',
env.spec.tags.get('wrapper_config.TimeLimit.max_episode_steps'))
if limit is not None:
env._max_episode_steps = limit
shape = options.get('environment/shape', (42, 42))
self._shape = shape[:2]
self._channels = 0 if len(shape) == 2 else shape[-1]
self.action_size = self._get_action_size(env)
if self.action_size != options.algorithm.output.action_size:
print(
'Algorithm expects different action size (%d) from gym (%d). \n'
'Please set correct action size in you configuration yaml.' %
(options.algorithm.output.action_size, self.action_size))
sys.exit(-1)
self.env = NoNegativeRewardEnv(env)
self._obs_buffer = deque(maxlen=2)
self.observation_space = Box(0.0, 255.0, shape)
self.observation_space.high[...] = 1.0
self._scale = (1.0 / 255.0)
self.reset()
def _run_deepmind_lab(self):
log.info('Run deepmind-lab, please wait, it may take a moment...')
try:
rlx_address = options.get('rlx_server_address', None)
if rlx_address is None:
rlx_address = options.get('relaax_rlx_server/bind',
'localhost:7001')
app_path = os.path.dirname(os.path.abspath(__file__))
config = os.path.abspath(os.path.join(app_path, '../app.yaml'))
headless = 'false' if self.show_ui else 'osmesa'
cmd = 'cd %s && bazel run :random_agent --define headless=%s' % (
self.lab_path, headless)
cmd = '%s --%s --app_path %s --config %s --show-ui %s --rlx-server-address %s' % \
(cmd, self.level_script, app_path, config, self.show_ui, rlx_address)
log.info(cmd)
subprocess.call(cmd, shell=True)
except subprocess.CalledProcessError as e:
log.info('Error while building deepmind-lab: %s' % str(e))
raise
def _get_rlx_address():
def parse_address(address):
try:
host, port = address.split(':')
return host, int(port)
except Exception:
raise ValueError("Can't parse RLX server address.")
host, port = parse_address(options.get('relaax_rlx_server/bind', '0.0.0.0:7001'))
if platform.system() == 'Linux':
host = '127.0.0.1'
elif host == '0.0.0.0':
import socket
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 80))
host = s.getsockname()[0]
s.close()
return '%s:%d' % (host, port)
def __init__(self):
self._fps = options.get('environment/fps', 20)
self._show_ui = options.get('environment/show_ui', False)
self._no_op_max = options.get('environment/no_op_max', 9)
self._frame_skip = options.get('environment/frame_skip', 4)
assert self._fps > 0, log.info(
'Frame per second rate should be above zero')
assert self._no_op_max > 0, log.info(
'Number of random actions at start should be above zero')
assert self._frame_skip > 0, log.info(
'Frame skipping rate should be above zero')
shape = options.get('environment/shape', (84, 84))
self._height, self._width = shape[0], shape[1]
self._channels = 0 if len(shape) == 2 else shape[-1]
action_size = options.get('environment/action_size', 'small')
if action_size in ACTIONS:
self._actions = list(ACTIONS[action_size].values())
else:
log.info('You\'ve provided an invalid action size. \n'
'Valid options are follows: \n {}'.format(ACTIONS.keys()))
if self._show_ui:
self._width = 640
self._height = 480
self.env = deepmind_lab.Lab(options.get('environment/level_script',
'nav_maze_static_01'),
['RGB_INTERLACED'],
config={
'fps': str(self._fps),
'width': str(self._width),
'height': str(self._height)
})
self._scale = (1.0 / 255.0)
self.reset()
def __init__(self):
super(Training, self).__init__()
self.gym = DoomEnv(level=options.get('environment/name',
'ppaquette/DoomMyWayHome-v0'))
def __init__(self):
super(Training, self).__init__()
self.episode_length = options.get('environment/episode_length', 5)
def __init__(self):
super(Training, self).__init__()
self.gym = GymEnv(env=options.get('environment/name', 'CartPole-v0'))
def __init__(self):
self.exploit = options.exploit
self.max_episodes = options.get('environment/max_episodes', 1)
self.infinite_run = options.get('environment/infinite_run', False)
self.agent = AgentProxy(options.rlx_server_address)
def __init__(self):
super(Training, self).__init__()
self.steps = options.get('environment/steps', 1000)
self.bandit = Bandit()
def __init__(self, image_name):
self.image_name = image_name
self.show_ui = options.get('show_ui', False)