def test_config_init(_):
c = TestARS.c
config = ARS.generate_config({})
config["frame_config"]["models"] = ["ActorDiscrete"]
config["frame_config"]["model_kwargs"] = [{
"state_dim": c.observe_dim,
"action_dim": c.action_num
}]
ars = ARS.init_from_config(config)
for at in ars.get_actor_types():
# get action will cause filters to initialize
_action = ars.act(
{"state": t.zeros([1, c.observe_dim], dtype=t.float32)}, at)
if at.startswith("neg"):
ars.store_reward(1.0, at)
else:
ars.store_reward(0.0, at)
ars.update()
return True
def ars_lr(device, dtype):
c = TestARS.c
actor = smw(
ActorDiscrete(c.observe_dim, c.action_num).type(dtype).to(device),
device, device)
lr_func = gen_learning_rate_func([(0, 1e-3), (200000, 3e-4)],
logger=default_logger)
servers = model_server_helper(model_num=1)
world = get_world()
ars_group = world.create_rpc_group("ars", ["0", "1", "2"])
ars = ARS(actor,
t.optim.SGD,
ars_group,
servers,
noise_size=1000000,
lr_scheduler=LambdaLR,
lr_scheduler_args=((lr_func, ), ))
return ars
def ars(device, dtype):
c = TestARS.c
actor = smw(
ActorDiscrete(c.observe_dim, c.action_num).type(dtype).to(device),
device, device)
servers = model_server_helper(model_num=1)
world = get_world()
ars_group = world.create_rpc_group("ars", ["0", "1", "2"])
ars = ARS(actor,
t.optim.SGD,
ars_group,
servers,
noise_std_dev=0.1,
learning_rate=0.1,
noise_size=1000000,
rollout_num=6,
used_rollout_num=6,
normalize_state=True)
return ars