def parse_args_uargs(self):
args, config = parse_args_uargs(self.args, [])
config = merge_dicts_smart(config, self.grid_config)
config = merge_dicts_smart(config, self.params)
if self.distr_info:
self.set_dist_env(config)
return args, config
def main(args, unknown_args):
args, config = parse_args_uargs(args, unknown_args)
set_global_seed(args.seed)
prepare_cudnn(args.deterministic, args.benchmark)
if args.logdir is not None:
os.makedirs(args.logdir, exist_ok=True)
dump_environment(config, args.logdir, args.configs)
if args.expdir is not None:
module = import_module(expdir=args.expdir) # noqa: F841
if args.logdir is not None:
dump_code(args.expdir, args.logdir)
env = ENVIRONMENTS.get_from_params(**config["environment"])
algorithm_name = config["algorithm"].pop("algorithm")
if algorithm_name in OFFPOLICY_ALGORITHMS_NAMES:
ALGORITHMS = OFFPOLICY_ALGORITHMS
trainer_fn = OffpolicyTrainer
sync_epoch = False
elif algorithm_name in ONPOLICY_ALGORITHMS_NAMES:
ALGORITHMS = ONPOLICY_ALGORITHMS
trainer_fn = OnpolicyTrainer
sync_epoch = True
else:
# @TODO: add registry for algorithms, trainers, samplers
raise NotImplementedError()
db_server = DATABASES.get_from_params(
**config.get("db", {}), sync_epoch=sync_epoch
)
algorithm_fn = ALGORITHMS.get(algorithm_name)
algorithm = algorithm_fn.prepare_for_trainer(env_spec=env, config=config)
if args.resume is not None:
checkpoint = utils.load_checkpoint(filepath=args.resume)
checkpoint = utils.any2device(checkpoint, utils.get_device())
algorithm.unpack_checkpoint(
checkpoint=checkpoint,
with_optimizer=False
)
monitoring_params = config.get("monitoring_params", None)
trainer = trainer_fn(
algorithm=algorithm,
env_spec=env,
db_server=db_server,
logdir=args.logdir,
monitoring_params=monitoring_params,
**config["trainer"],
)
trainer.run()
def main(args, unknown_args):
args, config = parse_args_uargs(args, unknown_args)
set_global_seed(args.seed)
if args.logdir is not None:
os.makedirs(args.logdir, exist_ok=True)
dump_config(config, args.logdir, args.configs)
if args.expdir is not None:
module = import_module(expdir=args.expdir) # noqa: F841
env = ENVIRONMENTS.get_from_params(**config["environment"])
algorithm_name = config["algorithm"].pop("algorithm")
if algorithm_name in OFFPOLICY_ALGORITHMS_NAMES:
ALGORITHMS = OFFPOLICY_ALGORITHMS
trainer_fn = OffpolicyTrainer
sync_epoch = False
weights_sync_mode = "critic" if env.discrete_actions else "actor"
elif algorithm_name in ONPOLICY_ALGORITHMS_NAMES:
ALGORITHMS = ONPOLICY_ALGORITHMS
trainer_fn = OnpolicyTrainer
sync_epoch = True
weights_sync_mode = "actor"
else:
# @TODO: add registry for algorithms, trainers, samplers
raise NotImplementedError()
db_server = DATABASES.get_from_params(
**config.get("db", {}), sync_epoch=sync_epoch
)
algorithm_fn = ALGORITHMS.get(algorithm_name)
algorithm = algorithm_fn.prepare_for_trainer(env_spec=env, config=config)
# if args.resume is not None:
# algorithm.load_checkpoint(filepath=args.resume)
trainer = trainer_fn(
algorithm=algorithm,
env_spec=env,
db_server=db_server,
logdir=args.logdir,
weights_sync_mode=weights_sync_mode,
**config["trainer"],
)
trainer.run()
def main(args, unknown_args):
args, config = parse_args_uargs(args, unknown_args)
set_global_seed(args.seed)
prepare_cudnn(args.deterministic, args.benchmark)
args.vis = args.vis or 0
args.infer = args.infer or 0
args.valid = args.valid or 0
args.train = args.train or 0
if args.expdir is not None:
module = import_module(expdir=args.expdir) # noqa: F841
environment_name = config["environment"].pop("environment")
environment_fn = ENVIRONMENTS.get(environment_name)
algorithm_name = config["algorithm"].pop("algorithm")
if algorithm_name in OFFPOLICY_ALGORITHMS_NAMES:
ALGORITHMS = OFFPOLICY_ALGORITHMS
sync_epoch = False
elif algorithm_name in ONPOLICY_ALGORITHMS_NAMES:
ALGORITHMS = ONPOLICY_ALGORITHMS
sync_epoch = True
else:
raise NotImplementedError()
algorithm_fn = ALGORITHMS.get(algorithm_name)
processes = []
sampler_id = args.sampler_id
def on_exit():
for p in processes:
p.terminate()
atexit.register(on_exit)
params = dict(
seed=args.seed,
logdir=args.logdir,
algorithm_fn=algorithm_fn,
environment_fn=environment_fn,
config=config,
resume=args.resume,
db=args.db,
sync_epoch=sync_epoch
)
if args.check:
mode = "train"
mode = "valid" if (args.valid is not None and args.valid > 0) else mode
mode = "infer" if (args.infer is not None and args.infer > 0) else mode
params_ = dict(
visualize=(args.vis is not None and args.vis > 0),
mode=mode,
id=sampler_id
)
run_sampler(**params, **params_)
return
for i in range(args.vis):
params_ = dict(
visualize=True, mode="infer", id=sampler_id, exploration_power=0.0
)
p = mp.Process(
target=run_sampler,
kwargs=dict(**params, **params_),
daemon=args.daemon,
)
p.start()
processes.append(p)
sampler_id += 1
time.sleep(args.run_delay)
for i in range(args.infer):
params_ = dict(
visualize=False,
mode="infer",
id=sampler_id,
exploration_power=0.0
)
p = mp.Process(
target=run_sampler,
kwargs=dict(**params, **params_),
daemon=args.daemon,
)
p.start()
processes.append(p)
sampler_id += 1
time.sleep(args.run_delay)
for i in range(args.valid):
params_ = dict(
visualize=False,
mode="valid",
id=sampler_id,
exploration_power=0.0
)
p = mp.Process(
target=run_sampler,
kwargs=dict(**params, **params_),
daemon=args.daemon,
)
p.start()
processes.append(p)
sampler_id += 1
time.sleep(args.run_delay)
for i in range(1, args.train + 1):
exploration_power = i / args.train
params_ = dict(
visualize=False,
mode="train",
id=sampler_id,
exploration_power=exploration_power
)
p = mp.Process(
target=run_sampler,
kwargs=dict(**params, **params_),
daemon=args.daemon,
)
p.start()
processes.append(p)
sampler_id += 1
time.sleep(args.run_delay)
for p in processes:
p.join()