def train_seq2reward_and_compute_reward_mse(
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_seq2reward_path: Optional[str] = None,
):
""" Train Seq2Reward Network and compute reward mse. """
env = Gym(env_name=env_name)
env.seed(SEED)
manager = model.value
trainer = manager.initialize_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,
)
fill_replay_buffer(env, test_replay_buffer, num_test_transitions)
if saved_seq2reward_path is None:
# train from scratch
trainer = train_seq2reward(
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.seq2reward_network.load_state_dict(torch.load(saved_seq2reward_path))
state_dim = env.observation_space.shape[0]
with torch.no_grad():
trainer.seq2reward_network.eval()
test_batch = test_replay_buffer.sample_transition_batch(
batch_size=test_replay_buffer.size
)
preprocessed_test_batch = trainer_preprocessor(test_batch)
adhoc_action_padding(preprocessed_test_batch, state_dim=state_dim)
losses = trainer.get_loss(preprocessed_test_batch)
detached_losses = losses.cpu().detach().item()
trainer.seq2reward_network.train()
return detached_losses
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 train_mdnrnn_and_train_on_embedded_env(
env_name: str,
embedding_model: ModelManager__Union,
num_embedding_train_transitions: int,
seq_len: int,
batch_size: int,
num_embedding_train_epochs: int,
train_model: ModelManager__Union,
num_state_embed_transitions: int,
num_agent_train_epochs: int,
num_agent_eval_epochs: int,
use_gpu: bool,
passing_score_bar: float,
# pyre-fixme[9]: saved_mdnrnn_path has type `str`; used as `None`.
saved_mdnrnn_path: str = None,
):
""" Train an agent on embedded states by the MDNRNN. """
env = Gym(env_name=env_name)
env.seed(SEED)
embedding_manager = embedding_model.value
embedding_trainer = embedding_manager.initialize_trainer(
use_gpu=use_gpu,
reward_options=RewardOptions(),
normalization_data_map=build_normalizer(env),
)
device = "cuda" if use_gpu else "cpu"
embedding_trainer_preprocessor = make_replay_buffer_trainer_preprocessor(
embedding_trainer,
# pyre-fixme[6]: Expected `device` for 2nd param but got `str`.
device,
env,
)
if saved_mdnrnn_path is None:
# train from scratch
embedding_trainer = train_mdnrnn(
env=env,
trainer=embedding_trainer,
trainer_preprocessor=embedding_trainer_preprocessor,
num_train_transitions=num_embedding_train_transitions,
seq_len=seq_len,
batch_size=batch_size,
num_train_epochs=num_embedding_train_epochs,
)
else:
# load a pretrained model, and just evaluate it
embedding_trainer.memory_network.mdnrnn.load_state_dict(
torch.load(saved_mdnrnn_path))
# create embedding dataset
embed_rb, state_min, state_max = create_embed_rl_dataset(
env=env,
memory_network=embedding_trainer.memory_network,
num_state_embed_transitions=num_state_embed_transitions,
batch_size=batch_size,
seq_len=seq_len,
hidden_dim=embedding_trainer.params.hidden_size,
use_gpu=use_gpu,
)
embed_env = StateEmbedEnvironment(
gym_env=env,
mdnrnn=embedding_trainer.memory_network,
max_embed_seq_len=seq_len,
state_min_value=state_min,
state_max_value=state_max,
)
agent_manager = train_model.value
agent_trainer = agent_manager.initialize_trainer(
use_gpu=use_gpu,
reward_options=RewardOptions(),
# pyre-fixme[6]: Expected `EnvWrapper` for 1st param but got
# `StateEmbedEnvironment`.
normalization_data_map=build_normalizer(embed_env),
)
device = "cuda" if use_gpu else "cpu"
agent_trainer_preprocessor = make_replay_buffer_trainer_preprocessor(
agent_trainer,
# pyre-fixme[6]: Expected `device` for 2nd param but got `str`.
device,
env,
)
num_batch_per_epoch = embed_rb.size // batch_size
# FIXME: This has to be wrapped in dataloader
for epoch in range(num_agent_train_epochs):
for _ in tqdm(range(num_batch_per_epoch), desc=f"epoch {epoch}"):
batch = embed_rb.sample_transition_batch(batch_size=batch_size)
preprocessed_batch = agent_trainer_preprocessor(batch)
# FIXME: This should be fitted with Lightning's trainer
agent_trainer.train(preprocessed_batch)
# evaluate model
rewards = []
policy = agent_manager.create_policy(serving=False)
# pyre-fixme[6]: Expected `EnvWrapper` for 1st param but got
# `StateEmbedEnvironment`.
agent = Agent.create_for_env(embed_env, policy=policy, device=device)
# num_processes=1 needed to avoid workers from dying on CircleCI tests
rewards = evaluate_for_n_episodes(
n=num_agent_eval_epochs,
# pyre-fixme[6]: Expected `EnvWrapper` for 2nd param but got
# `StateEmbedEnvironment`.
env=embed_env,
agent=agent,
num_processes=1,
)
assert (np.mean(rewards) >= passing_score_bar
), f"average reward doesn't pass our bar {passing_score_bar}"
return rewards
def test_pocman(self):
env = Gym(env_name="Pocman-v0")
env.seed(313)
mean_acc_reward = self._test_env(env)
assert -80 <= mean_acc_reward <= -70
def test_string_game(self):
env = Gym(env_name="StringGame-v0")
env.seed(313)
mean_acc_reward = self._test_env(env)
assert 0.1 >= mean_acc_reward
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}."