def run_by_mode(spec_file, spec_name, lab_mode):
logger.info(f'Running lab in mode: {lab_mode}')
spec = spec_util.get(spec_file, spec_name)
info_space = InfoSpace()
analysis.save_spec(spec, info_space, unit='experiment')
# '@' is reserved for 'enjoy@{prepath}'
os.environ['lab_mode'] = lab_mode.split('@')[0]
os.environ['PREPATH'] = util.get_prepath(spec, info_space)
reload(logger) # to set PREPATH properly
if lab_mode == 'search':
info_space.tick('experiment')
Experiment(spec, info_space).run()
elif lab_mode.startswith('train'):
if '@' in lab_mode:
prepath = lab_mode.split('@')[1]
spec, info_space = util.prepath_to_spec_info_space(prepath)
else:
info_space.tick('trial')
Trial(spec, info_space).run()
elif lab_mode.startswith('enjoy'):
prepath = lab_mode.split('@')[1]
spec, info_space = util.prepath_to_spec_info_space(prepath)
Session(spec, info_space).run()
elif lab_mode.startswith('enjoy'):
prepath = lab_mode.split('@')[1]
spec, info_space = util.prepath_to_spec_info_space(prepath)
Session(spec, info_space).run()
elif lab_mode == 'dev':
spec = util.override_dev_spec(spec)
info_space.tick('trial')
Trial(spec, info_space).run()
else:
logger.warn('lab_mode not recognized; must be one of `search, train, enjoy, benchmark, dev`.')
def test_session(test_spec, test_info_space):
test_info_space.tick('trial')
test_info_space.tick('session')
analysis.save_spec(test_spec, test_info_space, unit='trial')
session = Session(test_spec, test_info_space)
session_data = session.run()
assert isinstance(session_data, pd.DataFrame)
def test_experiment(test_info_space):
spec = spec_util.get('demo.json', 'dqn_cartpole')
analysis.save_spec(spec, test_info_space, unit='experiment')
spec = spec_util.override_test_spec(spec)
test_info_space.tick('experiment')
experiment_data = Experiment(spec, test_info_space).run()
assert isinstance(experiment_data, pd.DataFrame)
def test_trial_demo(test_info_space):
spec = spec_util.get('demo.json', 'dqn_cartpole')
analysis.save_spec(spec, test_info_space, unit='experiment')
spec = spec_util.override_test_spec(spec)
spec['meta']['eval_frequency'] = 1
test_info_space.tick('trial')
trial_data = Trial(spec, test_info_space).run()
assert isinstance(trial_data, pd.DataFrame)
def __init__(self, spec, info_space=None):
info_space = info_space or InfoSpace()
init_thread_vars(spec, info_space, unit='trial')
self.spec = spec
self.info_space = info_space
self.coor, self.index = self.info_space.get_coor_idx(self)
self.session_data_dict = {}
self.data = None
analysis.save_spec(spec, info_space, unit='trial')
logger.info(f'Initialized trial {self.index}')
def __init__(self, spec, info_space=None):
info_space = info_space or InfoSpace()
init_thread_vars(spec, info_space, unit='trial')
self.spec = spec
self.info_space = info_space
self.coor, self.index = self.info_space.get_coor_idx(self)
self.session_data_dict = {}
self.data = None
analysis.save_spec(spec, info_space, unit='trial')
logger.info(f'Initialized trial {self.index}')
def __init__(self, spec, info_space):
self.spec = spec
self.info_space = info_space
self.index = self.info_space.get('experiment')
self.trial_data_dict = {}
self.data = None
analysis.save_spec(spec, info_space, unit='experiment')
SearchClass = getattr(search, spec['meta'].get('search'))
self.search = SearchClass(self)
logger.info(f'Initialized experiment {self.index}')
def __init__(self, spec, info_space=None):
info_space = info_space or InfoSpace()
init_thread_vars(spec, info_space, unit='experiment')
self.spec = spec
self.info_space = info_space
self.coor, self.index = self.info_space.get_coor_idx(self)
self.trial_data_dict = {}
self.data = None
SearchClass = getattr(search, spec['meta'].get('search'))
self.search = SearchClass(self)
analysis.save_spec(spec, info_space, unit='experiment')
logger.info(f'Initialized experiment {self.index}')
def __init__(self, spec, info_space=None):
info_space = info_space or InfoSpace()
init_thread_vars(spec, info_space, unit='experiment')
self.spec = spec
self.info_space = info_space
self.coor, self.index = self.info_space.get_coor_idx(self)
self.trial_data_dict = {}
self.data = None
SearchClass = getattr(search, spec['meta'].get('search'))
self.search = SearchClass(self)
analysis.save_spec(spec, info_space, unit='experiment')
logger.info(f'Initialized experiment {self.index}')
def __init__(self, spec, info_space):
self.spec = spec
self.info_space = info_space
self.index = self.info_space.get('trial')
self.session_data_dict = {}
self.data = None
analysis.save_spec(spec, info_space, unit='trial')
self.is_singleton = util.is_singleton(
spec) # singleton mode as opposed to multi-agent-env space
self.SessionClass = Session if self.is_singleton else SpaceSession
self.mp_runner = init_run_session if self.is_singleton else init_run_space_session
logger.info(f'Initialized trial {self.index}')
def test_session_total_t(test_spec, test_info_space):
test_info_space.tick('trial')
test_info_space.tick('session')
analysis.save_spec(test_spec, test_info_space, unit='trial')
spec = deepcopy(test_spec)
env_spec = spec['env'][0]
env_spec['max_tick'] = 30
spec['meta']['max_tick_unit'] = 'total_t'
session = Session(spec, test_info_space)
assert session.env.max_tick_unit == 'total_t'
session_data = session.run()
assert isinstance(session_data, pd.DataFrame)
def __init__(self, spec, info_space):
self.spec = spec
self.info_space = info_space
self.index = self.info_space.get('trial')
self.session_data_dict = {}
self.data = None
analysis.save_spec(spec, info_space, unit='trial')
self.is_singleton = len(spec['agent']) == 1 and len(
spec['env']
) == 1 and spec['body'][
'num'] == 1 # singleton mode as opposed to multi-agent-env space
self.SessionClass = Session if self.is_singleton else SpaceSession
logger.info(f'Initialized trial {self.index}')
def run_new_mode(spec_file, spec_name, lab_mode):
'''Run to generate new data with `search, train, dev`'''
spec = spec_util.get(spec_file, spec_name)
info_space = InfoSpace()
analysis.save_spec(spec, info_space, unit='experiment') # first save the new spec
if lab_mode == 'search':
info_space.tick('experiment')
Experiment(spec, info_space).run()
elif lab_mode.startswith('train'):
info_space.tick('trial')
Trial(spec, info_space).run()
elif lab_mode == 'dev':
spec = spec_util.override_dev_spec(spec)
info_space.tick('trial')
Trial(spec, info_space).run()
else:
raise ValueError(f'Unrecognizable lab_mode not of {TRAIN_MODES}')
def test_trial(test_spec, test_info_space):
test_info_space.tick('trial')
analysis.save_spec(test_spec, test_info_space, unit='trial')
trial = Trial(test_spec, test_info_space)
trial_data = trial.run()
assert isinstance(trial_data, pd.DataFrame)
