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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
r"""Run training.
Choose training algorithm and task(s) and follow these examples.
Run synchronous policy gradient training locally:
CONFIG="agent=c(algorithm='pg'),env=c(task='reverse')"
OUT_DIR="/tmp/bf_pg_local"
rm -rf $OUT_DIR
bazel run -c opt single_task:run -- \
--alsologtostderr \
--config="$CONFIG" \
--max_npe=0 \
--logdir="$OUT_DIR" \
--summary_interval=1 \
--model_v=0
learning/brain/tensorboard/tensorboard.sh --port 12345 --logdir "$OUT_DIR"
Run genetic algorithm locally:
CONFIG="agent=c(algorithm='ga'),env=c(task='reverse')"
OUT_DIR="/tmp/bf_ga_local"
rm -rf $OUT_DIR
bazel run -c opt single_task:run -- \
--alsologtostderr \
--config="$CONFIG" \
--max_npe=0 \
--logdir="$OUT_DIR"
Run uniform random search locally:
CONFIG="agent=c(algorithm='rand'),env=c(task='reverse')"
OUT_DIR="/tmp/bf_rand_local"
rm -rf $OUT_DIR
bazel run -c opt single_task:run -- \
--alsologtostderr \
--config="$CONFIG" \
--max_npe=0 \
--logdir="$OUT_DIR"
"""
from absl import app
from absl import flags
from absl import logging
from single_task import defaults # brain coder
from single_task import ga_train # brain coder
from single_task import pg_train # brain coder
FLAGS = flags.FLAGS
flags.DEFINE_string('config', '', 'Configuration.')
flags.DEFINE_string(
'logdir', None, 'Absolute path where to write results.')
flags.DEFINE_integer('task_id', 0, 'ID for this worker.')
flags.DEFINE_integer('num_workers', 1, 'How many workers there are.')
flags.DEFINE_integer(
'max_npe', 0,
'NPE = number of programs executed. Maximum number of programs to execute '
'in each run. Training will complete when this threshold is reached. Set '
'to 0 for unlimited training.')
flags.DEFINE_integer(
'num_repetitions', 1,
'Number of times the same experiment will be run (globally across all '
'workers). Each run is independent.')
flags.DEFINE_string(
'log_level', 'INFO',
'The threshold for what messages will be logged. One of DEBUG, INFO, WARN, '
'ERROR, or FATAL.')
# To register an algorithm:
# 1) Add dependency in the BUILD file to this build rule.
# 2) Import the algorithm's module at the top of this file.
# 3) Add a new entry in the following dict. The key is the algorithm name
# (used to select the algorithm in the config). The value is the module
# defining the expected functions for training and tuning. See the docstring
# for `get_namespace` for further details.
ALGORITHM_REGISTRATION = {
'pg': pg_train,
'ga': ga_train,
'rand': ga_train,
}
def get_namespace(config_string):
"""Get namespace for the selected algorithm.
Users who want to add additional algorithm types should modify this function.
The algorithm's namespace should contain the following functions:
run_training: Run the main training loop.
define_tuner_hparam_space: Return the hparam tuning space for the algo.
write_hparams_to_config: Helper for tuning. Write hparams chosen for tuning
to the Config object.
Look at pg_train.py and ga_train.py for function signatures and
implementations.
Args:
config_string: String representation of a Config object. This will get
parsed into a Config in order to determine what algorithm to use.
Returns:
algorithm_namespace: The module corresponding to the algorithm given in the
config.
config: The Config object resulting from parsing `config_string`.
Raises:
ValueError: If config.agent.algorithm is not one of the registered
algorithms.
"""
config = defaults.default_config_with_updates(config_string)
if config.agent.algorithm not in ALGORITHM_REGISTRATION:
raise ValueError('Unknown algorithm type "%s"' % (config.agent.algorithm,))
else:
return ALGORITHM_REGISTRATION[config.agent.algorithm], config
def main(argv):
del argv # Unused.
logging.set_verbosity(FLAGS.log_level)
flags.mark_flag_as_required('logdir')
if FLAGS.num_workers <= 0:
raise ValueError('num_workers flag must be greater than 0.')
if FLAGS.task_id < 0:
raise ValueError('task_id flag must be greater than or equal to 0.')
if FLAGS.task_id >= FLAGS.num_workers:
raise ValueError(
'task_id flag must be strictly less than num_workers flag.')
ns, _ = get_namespace(FLAGS.config)
ns.run_training(is_chief=FLAGS.task_id == 0)
if __name__ == '__main__':
app.run(main)
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