def test_config_init(self, train_config):
c = train_config
config = DDPG.generate_config({})
config["frame_config"]["models"] = ["Actor", "Actor", "Critic", "Critic"]
config["frame_config"]["model_kwargs"] = [
{
"state_dim": c.observe_dim,
"action_dim": c.action_dim,
"action_range": c.action_range,
}
] * 2 + [{"state_dim": c.observe_dim, "action_dim": c.action_dim}] * 2
ddpg = DDPG.init_from_config(config)
old_state = state = t.zeros([1, c.observe_dim], dtype=t.float32)
action = t.zeros([1, c.action_dim], dtype=t.float32)
ddpg.store_transition(
{
"state": {"state": old_state},
"action": {"action": action},
"next_state": {"state": state},
"reward": 0,
"terminal": False,
}
)
ddpg.update()
def ddpg(self, train_config, device, dtype):
c = train_config
actor = smw(
Actor(c.observe_dim, c.action_dim, c.action_range).type(dtype).to(device),
device,
device,
)
actor_t = smw(
Actor(c.observe_dim, c.action_dim, c.action_range).type(dtype).to(device),
device,
device,
)
critic = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device, device
)
critic_t = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device, device
)
ddpg = DDPG(
actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
replay_device="cpu",
replay_size=c.replay_size,
)
return ddpg
def ddpg_disc(self, train_config, device, dtype):
# not used for training, only used for testing apis
c = train_config
actor = smw(
ActorDiscrete(c.observe_dim, c.action_dim).type(dtype).to(device),
device,
device,
)
actor_t = smw(
ActorDiscrete(c.observe_dim, c.action_dim).type(dtype).to(device),
device,
device,
)
critic = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device, device
)
critic_t = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device, device
)
ddpg = DDPG(
actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
replay_device="cpu",
replay_size=c.replay_size,
)
return ddpg
def ddpg_vis(self, train_config, device, dtype, tmpdir):
# not used for training, only used for testing apis
c = train_config
tmp_dir = tmpdir.make_numbered_dir()
actor = smw(
Actor(c.observe_dim, c.action_dim, c.action_range).type(dtype).to(device),
device,
device,
)
actor_t = smw(
Actor(c.observe_dim, c.action_dim, c.action_range).type(dtype).to(device),
device,
device,
)
critic = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device, device
)
critic_t = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device, device
)
ddpg = DDPG(
actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
replay_device="cpu",
replay_size=c.replay_size,
visualize=True,
visualize_dir=str(tmp_dir),
)
return ddpg
def ddpg_lr(self, train_config, device, dtype):
# not used for training, only used for testing apis
c = train_config
actor = smw(
ActorDiscrete(c.observe_dim, c.action_dim).type(dtype).to(device),
device,
device,
)
actor_t = smw(
ActorDiscrete(c.observe_dim, c.action_dim).type(dtype).to(device),
device,
device,
)
critic = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device, device
)
critic_t = smw(
Critic(c.observe_dim, c.action_dim).type(dtype).to(device), device, device
)
lr_func = gen_learning_rate_func([(0, 1e-3), (200000, 3e-4)], logger=logger)
with pytest.raises(TypeError, match="missing .+ positional argument"):
_ = DDPG(
actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
replay_device="cpu",
replay_size=c.replay_size,
lr_scheduler=LambdaLR,
)
ddpg = DDPG(
actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
replay_device="cpu",
replay_size=c.replay_size,
lr_scheduler=LambdaLR,
lr_scheduler_args=((lr_func,), (lr_func,)),
)
return ddpg
def ddpg_train(self, train_config):
c = train_config
# cpu is faster for testing full training.
actor = smw(Actor(c.observe_dim, c.action_dim, c.action_range), "cpu", "cpu")
actor_t = smw(Actor(c.observe_dim, c.action_dim, c.action_range), "cpu", "cpu")
critic = smw(Critic(c.observe_dim, c.action_dim), "cpu", "cpu")
critic_t = smw(Critic(c.observe_dim, c.action_dim), "cpu", "cpu")
ddpg = DDPG(
actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction="sum"),
replay_device="cpu",
replay_size=c.replay_size,
)
return ddpg
def ddpg(self, train_config):
c = train_config
actor = smw(
Actor(c.observe_dim, c.action_dim, c.action_range).to(c.device),
c.device, c.device)
actor_t = smw(
Actor(c.observe_dim, c.action_dim, c.action_range).to(c.device),
c.device, c.device)
critic = smw(
Critic(c.observe_dim, c.action_dim).to(c.device), c.device,
c.device)
critic_t = smw(
Critic(c.observe_dim, c.action_dim).to(c.device), c.device,
c.device)
ddpg = DDPG(actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
replay_device=c.device,
replay_size=c.replay_size)
return ddpg
def ddpg_disc(self, train_config):
# not used for training, only used for testing apis
c = train_config
actor = smw(
ActorDiscreet(c.observe_dim, c.action_dim).to(c.device), c.device,
c.device)
actor_t = smw(
ActorDiscreet(c.observe_dim, c.action_dim).to(c.device), c.device,
c.device)
critic = smw(
Critic(c.observe_dim, c.action_dim).to(c.device), c.device,
c.device)
critic_t = smw(
Critic(c.observe_dim, c.action_dim).to(c.device), c.device,
c.device)
ddpg = DDPG(actor,
actor_t,
critic,
critic_t,
t.optim.Adam,
nn.MSELoss(reduction='sum'),
replay_device=c.device,
replay_size=c.replay_size)
return ddpg