def main(args):
"""
Run an evaluation training.
:param args: Dict[str, Any]
:return:
"""
# construct logging objects
args = DotDict(args)
Init.print_ascii_logo()
logger = Init.setup_logger(args.logdir, "eval")
Init.log_args(logger, args)
R.load_extern_classes(args.logdir)
container = RenderContainer(
args.actor,
args.epoch,
args.start,
args.end,
logger,
args.logdir,
args.gpu_id,
args.seed,
args.manager,
)
try:
container.run()
finally:
container.close()
def main(args):
"""
Run an evaluation.
:param args: Dict[str, Any]
:return:
"""
args = DotDict(args)
Init.print_ascii_logo()
logger = Init.setup_logger(args.logdir, 'eval')
Init.log_args(logger, args)
R.load_extern_classes(args.logdir)
eval_container = EvalContainer(
args.actor,
args.epoch,
logger,
args.logdir,
args.gpu_id,
args.nb_episode,
args.start,
args.end,
args.seed,
args.manager
)
try:
eval_container.run()
finally:
eval_container.close()
def __init__(
self,
eval_actor,
epoch_id,
logger,
log_id_dir,
gpu_id,
nb_episode,
start,
end,
seed,
manager
):
self.log_dir_helper = log_dir_helper = LogDirHelper(log_id_dir)
self.train_args = train_args = log_dir_helper.load_args()
self.device = device = self._device_from_gpu_id(gpu_id)
self.logger = logger
if epoch_id:
epoch_ids = [epoch_id]
else:
epoch_ids = self.log_dir_helper.epochs()
epoch_ids = filter(lambda eid: eid >= start, epoch_ids)
if end != -1.:
epoch_ids = filter(lambda eid: eid <= end, epoch_ids)
epoch_ids = list(epoch_ids)
self.epoch_ids = epoch_ids
engine = REGISTRY.lookup_engine(train_args.env)
env_cls = REGISTRY.lookup_env(train_args.env)
mgr_cls = REGISTRY.lookup_manager(manager)
self.env_mgr = env_mgr = SubProcEnvManager.from_args(
self.train_args,
engine,
env_cls,
seed=seed,
nb_env=nb_episode
)
if train_args.agent:
agent = train_args.agent
else:
agent = train_args.actor_host
output_space = REGISTRY.lookup_output_space(
agent, env_mgr.action_space
)
actor_cls = REGISTRY.lookup_actor(eval_actor)
self.actor = actor_cls.from_args(
actor_cls.prompt(),
env_mgr.action_space
)
self.network = self._init_network(
train_args,
env_mgr.observation_space,
env_mgr.gpu_preprocessor,
output_space,
REGISTRY
).to(device)
def from_defaults(args):
if args.agent:
agent_cls = R.lookup_agent(args.agent)
agent_args = agent_cls.args
else:
h = R.lookup_actor(args.actor_host)
w = R.lookup_actor(args.actor_worker)
l = R.lookup_learner(args.learner)
e = R.lookup_exp(args.exp)
agent_args = {**h.args, **w.args, **l.args, **e.args}
env_cls = R.lookup_env(args.env)
rwdnorm_cls = R.lookup_reward_normalizer(args.rwd_norm)
env_args = env_cls.args
rwdnorm_args = rwdnorm_cls.args
if args.custom_network:
net_args = R.lookup_network(args.custom_network).args
else:
net_args = R.lookup_modular_args(args)
args = DotDict({
**args,
**agent_args,
**env_args,
**rwdnorm_args,
**net_args
})
return args
def from_prompt(args):
if args.agent:
agent_cls = R.lookup_agent(args.agent)
agent_args = agent_cls.prompt(provided=args)
else:
h = R.lookup_actor(args.actor_host)
w = R.lookup_actor(args.actor_worker)
l = R.lookup_learner(args.learner)
e = R.lookup_exp(args.exp)
agent_args = {
**h.prompt(args),
**w.prompt(args),
**l.prompt(args),
**e.prompt(args),
}
env_cls = R.lookup_env(args.env)
rwdnorm_cls = R.lookup_reward_normalizer(args.rwd_norm)
env_args = env_cls.prompt(provided=args)
rwdnorm_args = rwdnorm_cls.prompt(provided=args)
if args.custom_network:
net_args = R.lookup_network(args.custom_network).prompt()
else:
net_args = R.prompt_modular_args(args)
args = DotDict({
**args,
**agent_args,
**env_args,
**rwdnorm_args,
**net_args
})
return args
def main(local_args):
"""
Run distributed training.
:param local_args: Dict[str, Any]
:return:
"""
log_id_dir = local_args.log_id_dir
initial_step_count = local_args.initial_step_count
R.load_extern_classes(log_id_dir)
logger = Init.setup_logger(log_id_dir, "train{}".format(GLOBAL_RANK))
helper = LogDirHelper(log_id_dir)
with open(helper.args_file_path(), "r") as args_file:
args = DotDict(json.load(args_file))
if local_args.resume:
args = DotDict({**args, **vars(local_args)})
dist.init_process_group(
backend="nccl",
init_method=args.init_method,
world_size=WORLD_SIZE,
rank=LOCAL_RANK,
)
logger.info("Rank {} initialized.".format(GLOBAL_RANK))
if LOCAL_RANK == 0:
container = DistribHost(
args,
logger,
log_id_dir,
initial_step_count,
LOCAL_RANK,
GLOBAL_RANK,
WORLD_SIZE,
)
else:
container = DistribWorker(
args,
logger,
log_id_dir,
initial_step_count,
LOCAL_RANK,
GLOBAL_RANK,
WORLD_SIZE,
)
try:
container.run()
finally:
container.close()
def __init__(self, args, log_id_dir, initial_step_count, rank):
seed = args.seed \
if rank == 0 \
else args.seed + args.nb_env * rank
print('Worker {} using seed {}'.format(rank, seed))
# load saved registry classes
REGISTRY.load_extern_classes(log_id_dir)
# ENV
engine = REGISTRY.lookup_engine(args.env)
env_cls = REGISTRY.lookup_env(args.env)
mgr_cls = REGISTRY.lookup_manager(args.manager)
env_mgr = mgr_cls.from_args(args, engine, env_cls, seed=seed)
# NETWORK
torch.manual_seed(args.seed)
device = torch.device("cuda" if (torch.cuda.is_available()) else "cpu")
output_space = REGISTRY.lookup_output_space(args.actor_worker,
env_mgr.action_space)
if args.custom_network:
net_cls = REGISTRY.lookup_network(args.custom_network)
else:
net_cls = ModularNetwork
net = net_cls.from_args(args, env_mgr.observation_space, output_space,
env_mgr.gpu_preprocessor, REGISTRY)
actor_cls = REGISTRY.lookup_actor(args.actor_worker)
actor = actor_cls.from_args(args, env_mgr.action_space)
builder = actor_cls.exp_spec_builder(env_mgr.observation_space,
env_mgr.action_space,
net.internal_space(),
env_mgr.nb_env)
exp = REGISTRY.lookup_exp(args.exp).from_args(args, builder)
self.actor = actor
self.exp = exp.to(device)
self.nb_step = args.nb_step
self.env_mgr = env_mgr
self.nb_env = args.nb_env
self.network = net.to(device)
self.device = device
self.initial_step_count = initial_step_count
# TODO: this should be set to eval after some number of training steps
self.network.train()
# SETUP state variables for run
self.step_count = self.initial_step_count
self.global_step_count = self.initial_step_count
self.ep_rewards = torch.zeros(self.nb_env)
self.rank = rank
self.obs = dtensor_to_dev(self.env_mgr.reset(), self.device)
self.internals = listd_to_dlist([
self.network.new_internals(self.device) for _ in range(self.nb_env)
])
self.start_time = time()
self._weights_synced = False
def main(args):
"""
Run local training.
:param args: Dict[str, Any]
:return:
"""
args, log_id_dir, initial_step, logger = Init.main(MODE, args)
R.save_extern_classes(log_id_dir)
container = Local(args, logger, log_id_dir, initial_step)
if args.profile:
try:
from pyinstrument import Profiler
except:
raise ImportError(
'You must install pyinstrument to use profiling.')
container.nb_step = 10e3
profiler = Profiler()
profiler.start()
try:
container.run()
finally:
if args.profile:
profiler.stop()
print(profiler.output_text(unicode=True, color=True))
container.close()
if args.eval:
from adept.scripts.evaluate import main
eval_args = {
'log_id_dir': log_id_dir,
'gpu_id': 0,
'nb_episode': 30,
}
if args.custom_network:
eval_args['custom_network'] = args.custom_network
main(eval_args)
def register_learner(learner_cls):
from adept.registry import REGISTRY
REGISTRY.register_learner(learner_cls)
def __init__(
self,
args,
log_id_dir,
initial_step_count,
rank=0,
):
# ARGS TO STATE VARS
self._args = args
self.nb_learners = args.nb_learners
self.nb_workers = args.nb_workers
self.rank = rank
self.nb_step = args.nb_step
self.nb_env = args.nb_env
self.initial_step_count = initial_step_count
self.epoch_len = args.epoch_len
self.summary_freq = args.summary_freq
self.nb_learn_batch = args.nb_learn_batch
self.rollout_queue_size = args.rollout_queue_size
# can be none if rank != 0
self.log_id_dir = log_id_dir
# load saved registry classes
REGISTRY.load_extern_classes(log_id_dir)
# ENV (temporary)
env_cls = REGISTRY.lookup_env(args.env)
env = env_cls.from_args(args, 0)
env_action_space, env_observation_space, env_gpu_preprocessor = \
env.action_space, env.observation_space, env.gpu_preprocessor
env.close()
# NETWORK
torch.manual_seed(args.seed)
device = torch.device("cuda") # ray handles gpus
torch.backends.cudnn.benchmark = True
output_space = REGISTRY.lookup_output_space(
args.actor_worker, env_action_space)
if args.custom_network:
net_cls = REGISTRY.lookup_network(args.custom_network)
else:
net_cls = ModularNetwork
net = net_cls.from_args(
args,
env_observation_space,
output_space,
env_gpu_preprocessor,
REGISTRY
)
self.network = net.to(device)
# TODO: this is a hack, remove once queuer puts rollouts on the correct device
self.network.device = device
self.device = device
self.network.train()
# OPTIMIZER
def optim_fn(x):
return torch.optim.RMSprop(x, lr=args.lr, eps=1e-5, alpha=0.99)
if args.nb_learners > 1:
self.optimizer = NCCLOptimizer(optim_fn, self.network, self.nb_learners)
else:
self.optimizer = optim_fn(self.network.parameters())
# LEARNER / EXP
rwd_norm = REGISTRY.lookup_reward_normalizer(
args.rwd_norm).from_args(args)
actor_cls = REGISTRY.lookup_actor(args.actor_host)
builder = actor_cls.exp_spec_builder(
env.observation_space,
env.action_space,
net.internal_space(),
args.nb_env * args.nb_learn_batch
)
w_builder = REGISTRY.lookup_actor(args.actor_worker).exp_spec_builder(
env.observation_space,
env.action_space,
net.internal_space(),
args.nb_env
)
actor = actor_cls.from_args(args, env.action_space)
learner = REGISTRY.lookup_learner(args.learner).from_args(args, rwd_norm)
exp_cls = REGISTRY.lookup_exp(args.exp).from_args(args, builder)
self.actor = actor
self.learner = learner
self.exp = exp_cls.from_args(args, builder).to(device)
# Rank 0 setup, load network/optimizer and create SummaryWriter/Saver
if rank == 0:
if args.load_network:
self.network = self.load_network(self.network, args.load_network)
print('Reloaded network from {}'.format(args.load_network))
if args.load_optim:
self.optimizer = self.load_optim(self.optimizer, args.load_optim)
print('Reloaded optimizer from {}'.format(args.load_optim))
print('Network parameters: ' + str(self.count_parameters(net)))
self.summary_writer = SummaryWriter(log_id_dir)
self.saver = SimpleModelSaver(log_id_dir)
def register_actor(actor_cls):
from adept.registry import REGISTRY
REGISTRY.register_actor(actor_cls)
def register_manager(manager_cls):
from adept.registry import REGISTRY
REGISTRY.register_manager(manager_cls)
def register_submodule(submod_cls):
from adept.registry import REGISTRY
REGISTRY.register_submodule(submod_cls)
def register_network(network_cls):
from adept.registry import REGISTRY
REGISTRY.register_network(network_cls)
def __init__(self, args, logger, log_id_dir, initial_step_count):
# ENV
engine = REGISTRY.lookup_engine(args.env)
env_cls = REGISTRY.lookup_env(args.env)
mgr_cls = REGISTRY.lookup_manager(args.manager)
env_mgr = mgr_cls.from_args(args, engine, env_cls)
# NETWORK
torch.manual_seed(args.seed)
if torch.cuda.is_available() and args.gpu_id >= 0:
device = torch.device("cuda:{}".format(args.gpu_id))
torch.backends.cudnn.benchmark = True
else:
device = torch.device("cpu")
output_space = REGISTRY.lookup_output_space(args.agent,
env_mgr.action_space)
if args.custom_network:
net_cls = REGISTRY.lookup_network(args.custom_network)
else:
net_cls = ModularNetwork
net = net_cls.from_args(
args,
env_mgr.gpu_preprocessor.observation_space,
output_space,
env_mgr.gpu_preprocessor,
REGISTRY,
)
logger.info("Network parameters: " + str(self.count_parameters(net)))
def optim_fn(x):
if args.optim == "RMSprop":
return torch.optim.RMSprop(x, lr=args.lr, eps=1e-5, alpha=0.99)
elif args.optim == "Adam":
return torch.optim.Adam(x, lr=args.lr, eps=1e-5)
def warmup_schedule(back_step):
return back_step / args.warmup if back_step < args.warmup else 1.0
# AGENT
rwd_norm = REGISTRY.lookup_reward_normalizer(
args.rwd_norm).from_args(args)
agent_cls = REGISTRY.lookup_agent(args.agent)
builder = agent_cls.exp_spec_builder(
env_mgr.observation_space,
env_mgr.action_space,
net.internal_space(),
env_mgr.nb_env,
)
agent = agent_cls.from_args(args, rwd_norm, env_mgr.action_space,
builder)
self.agent = agent.to(device)
self.nb_step = args.nb_step
self.env_mgr = env_mgr
self.nb_env = args.nb_env
self.network = net.to(device)
self.optimizer = optim_fn(self.network.parameters())
self.scheduler = LambdaLR(self.optimizer, warmup_schedule)
self.device = device
self.initial_step_count = initial_step_count
self.log_id_dir = log_id_dir
self.epoch_len = args.epoch_len
self.summary_freq = args.summary_freq
self.logger = logger
self.summary_writer = SummaryWriter(log_id_dir)
self.saver = SimpleModelSaver(log_id_dir)
self.updater = LocalUpdater(self.optimizer, self.network,
args.grad_norm_clip)
if args.load_network:
self.network = self.load_network(self.network, args.load_network)
logger.info("Reloaded network from {}".format(args.load_network))
if args.load_optim:
self.optimizer = self.load_optim(self.optimizer, args.load_optim)
logger.info("Reloaded optimizer from {}".format(args.load_optim))
self.network.train()
def __init__(
self,
args,
logger,
log_id_dir,
initial_step_count,
local_rank,
global_rank,
world_size,
):
seed = (
args.seed
if global_rank == 0
else args.seed + args.nb_env * global_rank
)
logger.info("Using {} for rank {} seed.".format(seed, global_rank))
# ENV
engine = REGISTRY.lookup_engine(args.env)
env_cls = REGISTRY.lookup_env(args.env)
mgr_cls = REGISTRY.lookup_manager(args.manager)
env_mgr = mgr_cls.from_args(args, engine, env_cls, seed=seed)
# NETWORK
torch.manual_seed(args.seed)
device = torch.device("cuda:{}".format(local_rank))
output_space = REGISTRY.lookup_output_space(
args.agent, env_mgr.action_space
)
if args.custom_network:
net_cls = REGISTRY.lookup_network(args.custom_network)
else:
net_cls = ModularNetwork
net = net_cls.from_args(
args,
env_mgr.observation_space,
output_space,
env_mgr.gpu_preprocessor,
REGISTRY,
)
logger.info("Network parameters: " + str(self.count_parameters(net)))
def optim_fn(x):
return torch.optim.RMSprop(x, lr=args.lr, eps=1e-5, alpha=0.99)
# AGENT
rwd_norm = REGISTRY.lookup_reward_normalizer(args.rwd_norm).from_args(
args
)
agent_cls = REGISTRY.lookup_agent(args.agent)
builder = agent_cls.exp_spec_builder(
env_mgr.observation_space,
env_mgr.action_space,
net.internal_space(),
env_mgr.nb_env,
)
agent = agent_cls.from_args(
args, rwd_norm, env_mgr.action_space, builder
)
self.agent = agent
self.nb_step = args.nb_step
self.env_mgr = env_mgr
self.nb_env = args.nb_env
self.network = net.to(device)
self.optimizer = optim_fn(self.network.parameters())
self.device = device
self.initial_step_count = initial_step_count
self.log_id_dir = log_id_dir
self.epoch_len = args.epoch_len
self.summary_freq = args.summary_freq
self.logger = logger
self.summary_writer = SummaryWriter(
os.path.join(log_id_dir, "rank{}".format(global_rank))
)
self.saver = SimpleModelSaver(log_id_dir)
self.local_rank = local_rank
self.global_rank = global_rank
self.world_size = world_size
self.updater = DistribUpdater(
self.optimizer,
self.network,
args.grad_norm_clip,
world_size,
not args.no_divide,
)
if args.load_network:
self.network = self.load_network(self.network, args.load_network)
logger.info("Reloaded network from {}".format(args.load_network))
if args.load_optim:
self.optimizer = self.load_optim(self.optimizer, args.load_optim)
logger.info("Reloaded optimizer from {}".format(args.load_optim))
self.network.train()
def register_exp(exp_cls):
from adept.registry import REGISTRY
REGISTRY.register_exp(exp_cls)
def register_agent(agent_cls):
from adept.registry import REGISTRY
REGISTRY.register_agent(agent_cls)
def __init__(
self,
args,
logger,
log_id_dir,
initial_step_count,
local_rank,
global_rank,
world_size
):
seed = args.seed \
if global_rank == 0 \
else args.seed + args.nb_env * global_rank
logger.info('Using {} for rank {} seed.'.format(seed, global_rank))
# ENV
engine = REGISTRY.lookup_engine(args.env)
env_cls = REGISTRY.lookup_env(args.env)
env_mgr = SubProcEnvManager.from_args(args, engine, env_cls, seed=seed)
# NETWORK
torch.manual_seed(args.seed)
device = torch.device("cuda:{}".format(local_rank))
output_space = REGISTRY.lookup_output_space(
args.agent, env_mgr.action_space)
if args.custom_network:
net_cls = REGISTRY.lookup_network(args.custom_network)
else:
net_cls = ModularNetwork
net = net_cls.from_args(
args,
env_mgr.observation_space,
output_space,
env_mgr.gpu_preprocessor,
REGISTRY
)
def optim_fn(x):
return torch.optim.RMSprop(x, lr=args.lr, eps=1e-5, alpha=0.99)
# AGENT
rwd_norm = REGISTRY.lookup_reward_normalizer(
args.rwd_norm).from_args(args)
agent_cls = REGISTRY.lookup_agent(args.agent)
builder = agent_cls.exp_spec_builder(
env_mgr.observation_space,
env_mgr.action_space,
net.internal_space(),
env_mgr.nb_env
)
agent = agent_cls.from_args(
args,
rwd_norm,
env_mgr.action_space,
builder
)
self.agent = agent
self.nb_step = args.nb_step
self.env_mgr = env_mgr
self.nb_env = args.nb_env
self.network = net.to(device)
self.optimizer = optim_fn(self.network.parameters())
self.device = device
self.initial_step_count = initial_step_count
self.log_id_dir = log_id_dir
self.epoch_len = args.epoch_len
self.summary_freq = args.summary_freq
self.logger = logger
self.local_rank = local_rank
self.global_rank = global_rank
self.world_size = world_size
if args.load_network:
self.network = self.load_network(self.network, args.load_network)
logger.info('Reloaded network from {}'.format(args.load_network))
if args.load_optim:
self.optimizer = self.load_optim(self.optimizer, args.load_optim)
logger.info('Reloaded optimizer from {}'.format(args.load_optim))
self.network.train()
def main(args):
"""
Run actorlearner training.
:param args: Dict[str, Any]
:return:
"""
args, log_id_dir, initial_step, logger = Init.main(MODE, args)
R.save_extern_classes(log_id_dir)
# start ray
if args.ray_addr is not None:
ray.init(address=args.ray_addr)
logger.info('Using Ray on a cluster. Head node address: {}'.format(
args.ray_addr))
else:
logger.info('Using Ray on a single machine.')
ray.init()
# create a main learner which logs summaries and saves weights
main_learner_cls = ActorLearnerHost.as_remote(
num_cpus=args.learner_cpu_alloc, num_gpus=args.learner_gpu_alloc)
main_learner = main_learner_cls.remote(args,
log_id_dir,
initial_step,
rank=0)
# if multiple learners setup nccl
if args.nb_learners > 1:
# create N peer learners
peer_learners = []
for p_ind in range(args.nb_learners - 1):
remote_cls = ActorLearnerHost.as_remote(
num_cpus=args.learner_cpu_alloc,
num_gpus=args.learner_gpu_alloc)
# init
remote = remote_cls.remote(args,
log_id_dir,
initial_step,
rank=p_ind + 1)
peer_learners.append(remote)
# figure out main learner node ip
nccl_addr, nccl_ip, nccl_port = ray.get(
main_learner._rank0_nccl_port_init.remote())
# setup all nccls
nccl_inits = [
main_learner._nccl_init.remote(nccl_addr, nccl_ip, nccl_port)
]
nccl_inits.extend([
p._nccl_init.remote(nccl_addr, nccl_ip, nccl_port)
for p in peer_learners
])
# wait for all
ray.get(nccl_inits)
logger.info('NCCL initialized')
# have all sync parameters
[f._sync_peer_parameters.remote() for f in peer_learners]
main_learner._sync_peer_parameters.remote()
# else just 1 learner
else:
peer_learners = []
# create workers
workers = [
ActorLearnerWorker.as_remote(num_cpus=args.worker_cpu_alloc,
num_gpus=args.worker_gpu_alloc).remote(
args, log_id_dir, initial_step, w_ind)
for w_ind in range(args.nb_workers)
]
# synchronize worker variables
ray.get(
main_learner.synchronize_worker_parameters.remote(workers,
initial_step,
blocking=True))
try:
# startup the run method of all containers
runs = [main_learner.run.remote(workers, args.profile)]
runs.extend([f.run.remote(workers) for f in peer_learners])
done_training = ray.wait(runs)
finally:
closes = [main_learner.close.remote()]
closes.extend([f.close.remote() for f in peer_learners])
done_closing = ray.wait(closes)
if args.eval:
from adept.scripts.evaluate import main
eval_args = {
'log_id_dir': log_id_dir,
'gpu_id': 0,
'nb_episode': 30,
}
if args.custom_network:
eval_args['custom_network'] = args.custom_network
main(eval_args)
def register_env(env_cls):
from adept.registry import REGISTRY
REGISTRY.register_env(env_cls)
def main(args):
"""
Run distributed training.
:param args: Dict[str, Any]
:return:
"""
args = DotDict(args)
dist_world_size = args.nb_proc * args.nb_node
current_env = os.environ.copy()
current_env["MASTER_ADDR"] = args.master_addr
current_env["MASTER_PORT"] = str(args.master_port)
current_env["WORLD_SIZE"] = str(dist_world_size)
args, log_id_dir, initial_step, logger = Init.main(MODE, args)
R.save_extern_classes(log_id_dir)
processes = []
for local_rank in range(0, args.nb_proc):
# each process's rank
dist_rank = args.nb_proc * args.node_rank + local_rank
current_env["RANK"] = str(dist_rank)
current_env["LOCAL_RANK"] = str(local_rank)
# spawn the processes
if not args.resume:
cmd = [
sys.executable,
"-u",
"-m",
"adept.scripts._distrib",
"--log-id-dir={}".format(log_id_dir),
]
else:
cmd = [
sys.executable,
"-u",
"-m",
"adept.scripts._distrib",
"--log-id-dir={}".format(log_id_dir),
"--resume={}".format(True),
"--load-network={}".format(args.load_network),
"--load-optim={}".format(args.load_optim),
"--initial-step-count={}".format(initial_step),
"--init-method={}".format(args.init_method),
]
if args.custom_network:
cmd += ["--custom-network", args.custom_network]
process = subprocess.Popen(cmd, env=current_env)
processes.append(process)
for process in processes:
process.wait()
if args.eval:
from adept.scripts.evaluate import main
eval_args = {
"log_id_dir": log_id_dir,
"gpu_id": 0,
"nb_episode": 30,
}
if args.custom_network:
eval_args["custom_network"] = args.custom_network
main(eval_args)
def register_reward_norm(rwd_norm_cls):
from adept.registry import REGISTRY
REGISTRY.register_reward_normalizer(rwd_norm_cls)
