def create_for_env(
cls,
env: EnvWrapper,
policy: Optional[Policy],
*,
device: Union[str, torch.device] = "cpu",
obs_preprocessor=None,
action_extractor=None,
**kwargs,
):
"""
If `policy` is not given, we will try to create a random policy
"""
if isinstance(device, str):
device = torch.device(device)
if obs_preprocessor is None:
obs_preprocessor = env.get_obs_preprocessor(device=device)
if action_extractor is None:
action_extractor = env.get_action_extractor()
if policy is None:
policy = make_random_policy_for_env(env)
return cls(
policy,
obs_preprocessor=obs_preprocessor,
action_extractor=action_extractor,
**kwargs,
)
def offline_gym(
env_name: str,
pkl_path: str,
num_episodes_for_data_batch: int,
max_steps: Optional[int],
seed: Optional[int] = None,
):
"""
Generate samples from a DiscreteRandomPolicy on the Gym environment and
saves results in a pandas df parquet.
"""
initialize_seed(seed)
env: gym.Env = EnvFactory.make(env_name)
policy = make_random_policy_for_env(env)
dataset = RLDataset()
for i in range(num_episodes_for_data_batch):
logger.info(f"Starting episode {i}")
post_step = log_data_post_step(dataset=dataset, mdp_id=str(i), env=env)
agent = Agent.create_for_env(env,
policy,
post_transition_callback=post_step)
run_episode(env=env, agent=agent, max_steps=max_steps)
logger.info(f"Saving dataset with {len(dataset)} samples to {pkl_path}")
df = dataset.to_pandas_df()
df.to_pickle(pkl_path)
def run_test_offline(
env_name: str,
model: ModelManager__Union,
replay_memory_size: int,
num_batches_per_epoch: int,
num_train_epochs: int,
passing_score_bar: float,
num_eval_episodes: int,
minibatch_size: int,
use_gpu: bool,
):
env = Gym(env_name=env_name)
env.seed(SEED)
env.action_space.seed(SEED)
normalization = build_normalizer(env)
logger.info(f"Normalization is: \n{pprint.pformat(normalization)}")
manager = model.value
trainer = manager.initialize_trainer(
use_gpu=use_gpu,
reward_options=RewardOptions(),
normalization_data_map=normalization,
)
# first fill the replay buffer to burn_in
replay_buffer = ReplayBuffer(replay_capacity=replay_memory_size,
batch_size=minibatch_size)
# always fill full RB
random_policy = make_random_policy_for_env(env)
agent = Agent.create_for_env(env, policy=random_policy)
fill_replay_buffer(
env=env,
replay_buffer=replay_buffer,
desired_size=replay_memory_size,
agent=agent,
)
device = torch.device("cuda") if use_gpu else None
# pyre-fixme[6]: Expected `device` for 2nd param but got `Optional[torch.device]`.
trainer_preprocessor = make_replay_buffer_trainer_preprocessor(
trainer, device, env)
writer = SummaryWriter()
with summary_writer_context(writer):
for epoch in range(num_train_epochs):
logger.info(f"Evaluating before epoch {epoch}: ")
eval_rewards = evaluate_cem(env, manager, 1)
for _ in tqdm(range(num_batches_per_epoch)):
train_batch = replay_buffer.sample_transition_batch()
preprocessed_batch = trainer_preprocessor(train_batch)
trainer.train(preprocessed_batch)
logger.info(f"Evaluating after training for {num_train_epochs} epochs: ")
eval_rewards = evaluate_cem(env, manager, num_eval_episodes)
mean_rewards = np.mean(eval_rewards)
assert (mean_rewards >= passing_score_bar
), f"{mean_rewards} doesn't pass the bar {passing_score_bar}."
def fill_replay_buffer(env, replay_buffer: ReplayBuffer, desired_size: int):
""" Fill replay buffer with random transitions until size reaches desired_size. """
assert (
0 < desired_size and desired_size <= replay_buffer._replay_capacity
), f"It's not true that 0 < {desired_size} <= {replay_buffer._replay_capacity}."
assert replay_buffer.size < desired_size, (
f"Replay buffer already has {replay_buffer.size} elements. "
f"(more than desired_size = {desired_size})")
logger.info(
f" Starting to fill replay buffer using random policy to size: {desired_size}."
)
random_policy = make_random_policy_for_env(env)
post_step = add_replay_buffer_post_step(replay_buffer, env=env)
agent = Agent.create_for_env(env,
policy=random_policy,
post_transition_callback=post_step)
max_episode_steps = env.max_steps
with tqdm(
total=desired_size - replay_buffer.size,
desc=
f"Filling replay buffer from {replay_buffer.size} to size {desired_size} using random policy",
) as pbar:
mdp_id = 0
while replay_buffer.size < desired_size:
last_size = replay_buffer.size
max_steps = desired_size - replay_buffer.size - 1
if max_episode_steps is not None:
max_steps = min(max_episode_steps, max_steps)
run_episode(env=env,
agent=agent,
mdp_id=mdp_id,
max_steps=max_steps)
size_delta = replay_buffer.size - last_size
# The assertion below is commented out because it can't
# support input samples which has seq_len>1. This should be
# treated as a bug, and need to be fixed in the future.
# assert (
# size_delta >= 0
# ), f"size delta is {size_delta} which should be non-negative."
pbar.update(n=size_delta)
mdp_id += 1
if size_delta <= 0:
# replay buffer size isn't increasing... so stop early
break
if replay_buffer.size >= desired_size:
logger.info(
f"Successfully filled replay buffer to size: {replay_buffer.size}!"
)
else:
logger.info(
f"Stopped early and filled replay buffer to size: {replay_buffer.size}."
)
def fill_replay_buffer(env: Env, replay_buffer: ReplayBuffer,
desired_size: int):
""" Fill replay buffer with random transitions until size reaches desired_size. """
assert (
0 < desired_size and desired_size <= replay_buffer._replay_capacity
), f"It's not true that 0 < {desired_size} <= {replay_buffer._replay_capacity}."
assert replay_buffer.size < desired_size, (
f"Replay buffer already has {replay_buffer.size} elements. "
f"(more than desired_size = {desired_size})")
logger.info(f"Starting to fill replay buffer to size: {desired_size}.")
random_policy = make_random_policy_for_env(env)
post_step = add_replay_buffer_post_step(replay_buffer, env=env)
agent = Agent.create_for_env(env,
policy=random_policy,
post_transition_callback=post_step)
max_episode_steps = get_max_steps(env)
with tqdm(
total=desired_size - replay_buffer.size,
desc=
f"Filling replay buffer from {replay_buffer.size} to size {desired_size}",
) as pbar:
mdp_id = 0
while replay_buffer.size < desired_size:
last_size = replay_buffer.size
max_steps = desired_size - replay_buffer.size - 1
if max_episode_steps is not None:
max_steps = min(max_episode_steps, max_steps)
run_episode(env=env,
agent=agent,
mdp_id=mdp_id,
max_steps=max_steps)
size_delta = replay_buffer.size - last_size
assert (
size_delta >=
0), f"size delta is {size_delta} which should be non-negative."
pbar.update(n=size_delta)
mdp_id += 1
if size_delta == 0:
# replay buffer size isn't increasing... so stop early
break
if replay_buffer.size >= desired_size:
logger.info(
f"Successfully filled replay buffer to size: {replay_buffer.size}!"
)
else:
logger.info(
f"Stopped early and filled replay buffer to size: {replay_buffer.size}."
)
def offline_gym_random(
env_name: str,
pkl_path: str,
num_train_transitions: int,
max_steps: Optional[int],
seed: int = 1,
):
"""
Generate samples from a random Policy on the Gym environment and
saves results in a pandas df parquet.
"""
env = Gym(env_name=env_name)
random_policy = make_random_policy_for_env(env)
agent = Agent.create_for_env(env, policy=random_policy)
return _offline_gym(env, agent, pkl_path, num_train_transitions, max_steps,
seed)
def train_mdnrnn(
env: EnvWrapper,
trainer: MDNRNNTrainer,
trainer_preprocessor,
num_train_transitions: int,
seq_len: int,
batch_size: int,
num_train_epochs: int,
# for optional validation
test_replay_buffer=None,
):
train_replay_buffer = ReplayBuffer(
replay_capacity=num_train_transitions,
batch_size=batch_size,
stack_size=seq_len,
return_everything_as_stack=True,
)
random_policy = make_random_policy_for_env(env)
agent = Agent.create_for_env(env, policy=random_policy)
fill_replay_buffer(env, train_replay_buffer, num_train_transitions, agent)
num_batch_per_epoch = train_replay_buffer.size // batch_size
logger.info("Made RBs, starting to train now!")
optimizer = trainer.configure_optimizers()[0]
for _ in range(num_train_epochs):
for i in range(num_batch_per_epoch):
batch = train_replay_buffer.sample_transition_batch(batch_size=batch_size)
preprocessed_batch = trainer_preprocessor(batch)
loss = next(trainer.train_step_gen(preprocessed_batch, i))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# validation
if test_replay_buffer is not None:
with torch.no_grad():
trainer.memory_network.mdnrnn.eval()
test_batch = test_replay_buffer.sample_transition_batch(
batch_size=batch_size
)
preprocessed_test_batch = trainer_preprocessor(test_batch)
valid_losses = trainer.get_loss(preprocessed_test_batch)
trainer.memory_network.mdnrnn.train()
return trainer
def create_embed_rl_dataset(
env: EnvWrapper,
memory_network: MemoryNetwork,
num_state_embed_transitions: int,
batch_size: int,
seq_len: int,
hidden_dim: int,
use_gpu: bool,
):
assert isinstance(env.action_space, gym.spaces.Discrete)
assert isinstance(env.observation_space, gym.spaces.Box)
assert len(env.observation_space.shape) == 1
logger.info("Starting to create embedded RL Dataset!")
# seqlen+1 because MDNRNN embeds the first seq_len steps and then
# the embedded state will be concatenated with the last step
# Ie.. (o1,o2,...,on) -> RNN -> h1,h2,...,hn
# and we set s_{n+1} = [o_{n+1}, h_n]
embed_env = StateEmbedEnvironment(
gym_env=env, mdnrnn=memory_network, max_embed_seq_len=seq_len + 1
)
# now create a filled replay buffer of embeddings
# new obs shape dim = state_dim + hidden_dim
embed_rb = ReplayBuffer(
replay_capacity=num_state_embed_transitions, batch_size=batch_size, stack_size=1
)
random_policy = make_random_policy_for_env(env)
agent = Agent.create_for_env(env, policy=random_policy)
fill_replay_buffer(
env=embed_env,
replay_buffer=embed_rb,
desired_size=num_state_embed_transitions,
agent=agent,
)
batch = embed_rb.sample_transition_batch(batch_size=num_state_embed_transitions)
state_min = min(batch.state.min(), batch.next_state.min()).item()
state_max = max(batch.state.max(), batch.next_state.max()).item()
logger.info(
f"Finished making embed dataset with size {embed_rb.size}, "
f"min {state_min}, max {state_max}"
)
return embed_rb, state_min, state_max
def train_mdnrnn_and_compute_feature_stats(
env_name: str,
model: ModelManager__Union,
num_train_transitions: int,
num_test_transitions: int,
seq_len: int,
batch_size: int,
num_train_epochs: int,
use_gpu: bool,
saved_mdnrnn_path: Optional[str] = None,
):
"""Train MDNRNN Memory Network and compute feature importance/sensitivity."""
env: gym.Env = Gym(env_name=env_name)
env.seed(SEED)
manager = model.value
trainer = manager.build_trainer(
use_gpu=use_gpu,
reward_options=RewardOptions(),
normalization_data_map=build_normalizer(env),
)
device = "cuda" if use_gpu else "cpu"
# pyre-fixme[6]: Expected `device` for 2nd param but got `str`.
trainer_preprocessor = make_replay_buffer_trainer_preprocessor(trainer, device, env)
test_replay_buffer = ReplayBuffer(
replay_capacity=num_test_transitions,
batch_size=batch_size,
stack_size=seq_len,
return_everything_as_stack=True,
)
random_policy = make_random_policy_for_env(env)
agent = Agent.create_for_env(env, policy=random_policy)
fill_replay_buffer(env, test_replay_buffer, num_test_transitions, agent)
if saved_mdnrnn_path is None:
# train from scratch
trainer = train_mdnrnn(
env=env,
trainer=trainer,
trainer_preprocessor=trainer_preprocessor,
num_train_transitions=num_train_transitions,
seq_len=seq_len,
batch_size=batch_size,
num_train_epochs=num_train_epochs,
test_replay_buffer=test_replay_buffer,
)
else:
# load a pretrained model, and just evaluate it
trainer.memory_network.mdnrnn.load_state_dict(torch.load(saved_mdnrnn_path))
with torch.no_grad():
trainer.memory_network.mdnrnn.eval()
test_batch = test_replay_buffer.sample_transition_batch(
batch_size=test_replay_buffer.size
)
preprocessed_test_batch = trainer_preprocessor(test_batch)
feature_importance = calculate_feature_importance(
env=env,
trainer=trainer,
use_gpu=use_gpu,
test_batch=preprocessed_test_batch,
)
feature_sensitivity = calculate_feature_sensitivity(
env=env,
trainer=trainer,
use_gpu=use_gpu,
test_batch=preprocessed_test_batch,
)
trainer.memory_network.mdnrnn.train()
return feature_importance, feature_sensitivity
def run_test_offline(
env_name: str,
model: ModelManager__Union,
replay_memory_size: int,
num_batches_per_epoch: int,
num_train_epochs: int,
passing_score_bar: float,
num_eval_episodes: int,
minibatch_size: int,
use_gpu: bool,
):
env = Gym(env_name=env_name)
env.seed(SEED)
env.action_space.seed(SEED)
normalization = build_normalizer(env)
logger.info(f"Normalization is: \n{pprint.pformat(normalization)}")
manager = model.value
trainer = manager.build_trainer(
use_gpu=use_gpu,
reward_options=RewardOptions(),
normalization_data_map=normalization,
)
# first fill the replay buffer to burn_in
replay_buffer = ReplayBuffer(
replay_capacity=replay_memory_size, batch_size=minibatch_size
)
# always fill full RB
random_policy = make_random_policy_for_env(env)
agent = Agent.create_for_env(env, policy=random_policy)
fill_replay_buffer(
env=env,
replay_buffer=replay_buffer,
desired_size=replay_memory_size,
agent=agent,
)
device = torch.device("cuda") if use_gpu else None
dataset = OfflineReplayBufferDataset.create_for_trainer(
trainer,
env,
replay_buffer,
batch_size=minibatch_size,
num_batches=num_batches_per_epoch,
device=device,
)
data_loader = torch.utils.data.DataLoader(dataset, collate_fn=identity_collate)
pl_trainer = pl.Trainer(
max_epochs=num_train_epochs,
gpus=int(use_gpu),
deterministic=True,
default_root_dir=f"lightning_log_{str(uuid.uuid4())}",
)
pl_trainer.fit(trainer, data_loader)
logger.info(f"Evaluating after training for {num_train_epochs} epochs: ")
eval_rewards = evaluate_cem(env, manager, trainer, num_eval_episodes)
mean_rewards = np.mean(eval_rewards)
assert (
mean_rewards >= passing_score_bar
), f"{mean_rewards} doesn't pass the bar {passing_score_bar}."
def create_df_from_replay_buffer(
env,
problem_domain: ProblemDomain,
desired_size: int,
multi_steps: Optional[int],
ds: str,
) -> pd.DataFrame:
# fill the replay buffer
set_seed(env, SEED)
if multi_steps is None:
update_horizon = 1
return_as_timeline_format = False
else:
update_horizon = multi_steps
return_as_timeline_format = True
is_multi_steps = multi_steps is not None
replay_buffer = ReplayBuffer(
replay_capacity=desired_size,
batch_size=1,
update_horizon=update_horizon,
return_as_timeline_format=return_as_timeline_format,
)
random_policy = make_random_policy_for_env(env)
agent = Agent.create_for_env(env, policy=random_policy)
fill_replay_buffer(env, replay_buffer, desired_size, agent)
batch = replay_buffer.sample_all_valid_transitions()
n = batch.state.shape[0]
logger.info(f"Creating df of size {n}.")
def discrete_feat_transform(elem) -> str:
"""query data expects str format"""
return str(elem.item())
def continuous_feat_transform(elem: List[float]) -> Dict[int, float]:
"""query data expects sparse format"""
assert isinstance(elem, torch.Tensor), f"{type(elem)} isn't tensor"
assert len(elem.shape) == 1, f"{elem.shape} isn't 1-dimensional"
return {i: s.item() for i, s in enumerate(elem)}
def make_parametric_feat_transform(one_hot_dim: int):
"""one-hot and then continuous_feat_transform"""
def transform(elem) -> Dict[int, float]:
elem_tensor = torch.tensor(elem.item())
one_hot_feat = F.one_hot(elem_tensor, one_hot_dim).float()
return continuous_feat_transform(one_hot_feat)
return transform
state_features = feature_transform(batch.state, continuous_feat_transform)
next_state_features = feature_transform(
batch.next_state,
continuous_feat_transform,
is_next_with_multi_steps=is_multi_steps,
)
if problem_domain == ProblemDomain.DISCRETE_ACTION:
# discrete action is str
action = feature_transform(batch.action, discrete_feat_transform)
next_action = feature_transform(
batch.next_action,
discrete_feat_transform,
is_next_with_multi_steps=is_multi_steps,
replace_when_terminal="",
terminal=batch.terminal,
)
elif problem_domain == ProblemDomain.PARAMETRIC_ACTION:
# continuous action is Dict[int, double]
assert isinstance(env.action_space, gym.spaces.Discrete)
parametric_feat_transform = make_parametric_feat_transform(env.action_space.n)
action = feature_transform(batch.action, parametric_feat_transform)
next_action = feature_transform(
batch.next_action,
parametric_feat_transform,
is_next_with_multi_steps=is_multi_steps,
replace_when_terminal={},
terminal=batch.terminal,
)
elif problem_domain == ProblemDomain.CONTINUOUS_ACTION:
action = feature_transform(batch.action, continuous_feat_transform)
next_action = feature_transform(
batch.next_action,
continuous_feat_transform,
is_next_with_multi_steps=is_multi_steps,
replace_when_terminal={},
terminal=batch.terminal,
)
elif problem_domain == ProblemDomain.MDN_RNN:
action = feature_transform(batch.action, discrete_feat_transform)
assert multi_steps is not None
next_action = feature_transform(
batch.next_action,
discrete_feat_transform,
is_next_with_multi_steps=True,
replace_when_terminal="",
terminal=batch.terminal,
)
else:
raise NotImplementedError(f"model type: {problem_domain}.")
if multi_steps is None:
time_diff = [1] * n
reward = batch.reward.squeeze(1).tolist()
metrics = [{"reward": r} for r in reward]
else:
time_diff = [[1] * len(ns) for ns in next_state_features]
reward = [reward_list.tolist() for reward_list in batch.reward]
metrics = [
[{"reward": r.item()} for r in reward_list] for reward_list in batch.reward
]
# TODO(T67265031): change this to int
mdp_id = [str(i.item()) for i in batch.mdp_id]
sequence_number = batch.sequence_number.squeeze(1).tolist()
# in the product data, all sequence_number_ordinal start from 1.
# So to be consistent with the product data.
sequence_number_ordinal = (batch.sequence_number.squeeze(1) + 1).tolist()
action_probability = batch.log_prob.exp().squeeze(1).tolist()
df_dict = {
"state_features": state_features,
"next_state_features": next_state_features,
"action": action,
"next_action": next_action,
"reward": reward,
"action_probability": action_probability,
"metrics": metrics,
"time_diff": time_diff,
"mdp_id": mdp_id,
"sequence_number": sequence_number,
"sequence_number_ordinal": sequence_number_ordinal,
"ds": [ds] * n,
}
if problem_domain == ProblemDomain.PARAMETRIC_ACTION:
# Possible actions are List[Dict[int, float]]
assert isinstance(env.action_space, gym.spaces.Discrete)
possible_actions = [{i: 1.0} for i in range(env.action_space.n)]
elif problem_domain == ProblemDomain.DISCRETE_ACTION:
# Possible actions are List[str]
assert isinstance(env.action_space, gym.spaces.Discrete)
possible_actions = [str(i) for i in range(env.action_space.n)]
elif problem_domain == ProblemDomain.MDN_RNN:
# Possible actions are List[str]
assert isinstance(env.action_space, gym.spaces.Discrete)
possible_actions = [str(i) for i in range(env.action_space.n)]
# these are fillers, which should have correct shape
pa_features = range(n)
pna_features = time_diff
if problem_domain in (
ProblemDomain.DISCRETE_ACTION,
ProblemDomain.PARAMETRIC_ACTION,
ProblemDomain.MDN_RNN,
):
def pa_transform(x):
return possible_actions
df_dict["possible_actions"] = feature_transform(pa_features, pa_transform)
df_dict["possible_next_actions"] = feature_transform(
pna_features,
pa_transform,
is_next_with_multi_steps=is_multi_steps,
replace_when_terminal=[],
terminal=batch.terminal,
)
df = pd.DataFrame(df_dict)
# validate df
validate_mdp_ids_seq_nums(df)
# shuffling (sample the whole batch)
df = df.reindex(np.random.permutation(df.index))
return df
def run_test_replay_buffer(
env: Env__Union,
model: ModelManager__Union,
replay_memory_size: int,
train_every_ts: int,
train_after_ts: int,
num_train_episodes: int,
passing_score_bar: float,
num_eval_episodes: int,
use_gpu: bool,
minibatch_size: Optional[int] = None,
):
"""
Run an online learning test with a replay buffer. The replay buffer is pre-filled, then the training starts.
Each transition is added to the replay buffer immediately after it takes place.
"""
env = env.value
pl.seed_everything(SEED)
env.seed(SEED)
env.action_space.seed(SEED)
normalization = build_normalizer(env)
logger.info(f"Normalization is: \n{pprint.pformat(normalization)}")
manager = model.value
trainer = manager.build_trainer(
use_gpu=use_gpu,
normalization_data_map=normalization,
)
training_policy = manager.create_policy(trainer, serving=False)
if not isinstance(trainer, pl.LightningModule):
if minibatch_size is None:
minibatch_size = trainer.minibatch_size
assert minibatch_size == trainer.minibatch_size
assert minibatch_size is not None
replay_buffer = ReplayBuffer(replay_capacity=replay_memory_size,
batch_size=minibatch_size)
device = torch.device("cuda") if use_gpu else torch.device("cpu")
# first fill the replay buffer using random policy
train_after_ts = max(train_after_ts, minibatch_size)
random_policy = make_random_policy_for_env(env)
agent = Agent.create_for_env(env, policy=random_policy)
fill_replay_buffer(
env=env,
replay_buffer=replay_buffer,
desired_size=train_after_ts,
agent=agent,
)
agent = Agent.create_for_env(env, policy=training_policy, device=device)
# TODO: Simplify this setup by creating LightningDataModule
dataset = ReplayBufferDataset.create_for_trainer(
trainer,
env,
agent,
replay_buffer,
batch_size=minibatch_size,
training_frequency=train_every_ts,
num_episodes=num_train_episodes,
max_steps=200,
device=device,
)
data_loader = torch.utils.data.DataLoader(dataset,
collate_fn=identity_collate)
pl_trainer = pl.Trainer(
max_epochs=1,
gpus=int(use_gpu),
deterministic=True,
default_root_dir=f"lightning_log_{str(uuid.uuid4())}",
)
# Note: the fit() function below also evaluates the agent along the way
# and adds the new transitions to the replay buffer, so it is training
# on incrementally larger and larger buffers.
pl_trainer.fit(trainer, data_loader)
# TODO: Also check train_reward
serving_policy = manager.create_policy(
trainer, serving=True, normalization_data_map=normalization)
eval_rewards = eval_policy(env,
serving_policy,
num_eval_episodes,
serving=True)
assert (
eval_rewards.mean() >= passing_score_bar
), f"Eval reward is {eval_rewards.mean()}, less than < {passing_score_bar}.\n"
