def skewfit_experiment(variant):
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.online_vae_replay_buffer \
import OnlineVaeRelabelingBuffer
from rlkit.torch.networks import FlattenMlp
from rlkit.torch.sac.policies import TanhGaussianPolicy
import rlkit.torch.vae.vae_schedules as vae_schedules
#### getting parameter for training VAE and RIG
env = get_envs(variant)
observation_key = variant.get('observation_key', 'latent_observation')
desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal')
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
obs_dim = (env.observation_space.spaces[observation_key].low.size +
env.observation_space.spaces[desired_goal_key].low.size)
action_dim = env.action_space.low.size
hidden_sizes = variant.get('hidden_sizes', [400, 300])
replay_buffer_kwargs = variant.get(
'replay_buffer_kwargs',
dict(
start_skew_epoch=10,
max_size=int(100000),
fraction_goals_rollout_goals=0.2,
fraction_goals_env_goals=0.5,
exploration_rewards_type='None',
vae_priority_type='vae_prob',
priority_function_kwargs=dict(
sampling_method='importance_sampling',
decoder_distribution='gaussian_identity_variance',
num_latents_to_sample=10,
),
power=0,
relabeling_goal_sampling_mode='vae_prior',
))
online_vae_trainer_kwargs = variant.get('online_vae_trainer_kwargs',
dict(beta=20, lr=1e-3))
max_path_length = variant.get('max_path_length', 50)
algo_kwargs = variant.get(
'algo_kwargs',
dict(
batch_size=1024,
num_epochs=1000,
num_eval_steps_per_epoch=500,
num_expl_steps_per_train_loop=500,
num_trains_per_train_loop=1000,
min_num_steps_before_training=10000,
vae_training_schedule=vae_schedules.custom_schedule_2,
oracle_data=False,
vae_save_period=50,
parallel_vae_train=False,
))
twin_sac_trainer_kwargs = variant.get(
'twin_sac_trainer_kwargs',
dict(
discount=0.99,
reward_scale=1,
soft_target_tau=1e-3,
target_update_period=1, # 1
use_automatic_entropy_tuning=True,
))
############################################################################
qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes)
qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes)
target_qf1 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes)
target_qf2 = FlattenMlp(input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes)
policy = TanhGaussianPolicy(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=hidden_sizes)
vae = variant['vae_model']
# create a replay buffer for training an online VAE
replay_buffer = OnlineVaeRelabelingBuffer(
vae=vae,
env=env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
**replay_buffer_kwargs)
# create an online vae_trainer to train vae on the fly
vae_trainer = ConvVAETrainer(variant['vae_train_data'],
variant['vae_test_data'], vae,
**online_vae_trainer_kwargs)
# create a SACTrainer to learn a soft Q-function and appropriate policy
trainer = SACTrainer(env=env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**twin_sac_trainer_kwargs)
trainer = HERTrainer(trainer)
eval_path_collector = VAEWrappedEnvPathCollector(
variant.get('evaluation_goal_sampling_mode', 'reset_of_env'),
env,
MakeDeterministic(policy),
max_path_length,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
expl_path_collector = VAEWrappedEnvPathCollector(
variant.get('exploration_goal_sampling_mode', 'vae_prior'),
env,
policy,
max_path_length,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
algorithm = OnlineVaeAlgorithm(
trainer=trainer,
exploration_env=env,
evaluation_env=env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
vae=vae,
vae_trainer=vae_trainer,
max_path_length=max_path_length,
**algo_kwargs)
if variant['custom_goal_sampler'] == 'replay_buffer':
env.custom_goal_sampler = replay_buffer.sample_buffer_goals
algorithm.to(ptu.device)
vae.to(ptu.device)
algorithm.train()
def skewfit_experiment(variant, other_variant):
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.online_vae_replay_buffer import \
OnlineVaeRelabelingBuffer
from rlkit.torch.networks import FlattenMlp
from rlkit.torch.sac.policies import TanhGaussianPolicy
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
skewfit_preprocess_variant(variant)
env = get_envs(variant)
uniform_dataset_fn = variant.get('generate_uniform_dataset_fn', None)
if uniform_dataset_fn:
uniform_dataset = uniform_dataset_fn(
**variant['generate_uniform_dataset_kwargs'])
else:
uniform_dataset = None
observation_key = variant.get('observation_key', 'latent_observation')
desired_goal_key = variant.get('desired_goal_key', 'latent_desired_goal')
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
obs_dim = (env.observation_space.spaces[observation_key].low.size +
env.observation_space.spaces[desired_goal_key].low.size)
action_dim = env.action_space.low.size
hidden_sizes = variant.get('hidden_sizes', [400, 300])
qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
target_qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
target_qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
policy = TanhGaussianPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=hidden_sizes,
)
vae = env.vae
replay_buffer = OnlineVaeRelabelingBuffer(
vae=env.vae,
env=env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
**variant['replay_buffer_kwargs'])
vae_trainer = ConvVAETrainer(variant['vae_train_data'],
variant['vae_test_data'], env.vae,
other_variant,
**variant['online_vae_trainer_kwargs'])
assert 'vae_training_schedule' not in variant, "Just put it in algo_kwargs"
max_path_length = variant['max_path_length']
trainer = SACTrainer(env=env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['twin_sac_trainer_kwargs'])
trainer = HERTrainer(trainer)
eval_path_collector = VAEWrappedEnvPathCollector(
variant['evaluation_goal_sampling_mode'],
env,
MakeDeterministic(policy),
max_path_length,
other_variant=other_variant,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
expl_path_collector = VAEWrappedEnvPathCollector(
variant['exploration_goal_sampling_mode'],
env,
policy,
max_path_length,
other_variant=other_variant,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
algorithm = OnlineVaeAlgorithm(
trainer=trainer,
exploration_env=env,
evaluation_env=env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
vae=vae,
vae_trainer=vae_trainer,
uniform_dataset=uniform_dataset,
max_path_length=max_path_length,
**variant['algo_kwargs'])
if variant['custom_goal_sampler'] == 'replay_buffer':
env.custom_goal_sampler = replay_buffer.sample_buffer_goals
algorithm.to(ptu.device)
vae.to(ptu.device)
algorithm.train()
def skewfit_experiment(cfgs):
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.online_vae_replay_buffer import \
OnlineVaeRelabelingBuffer
from rlkit.torch.networks import FlattenMlp
from rlkit.torch.sac.policies import TanhGaussianPolicy
from rlkit.torch.vae.vae_trainer import ConvVAETrainer
skewfit_preprocess_variant(cfgs)
env = get_envs(cfgs)
# TODO
uniform_dataset_fn = cfgs.GENERATE_VAE_DATASET.get(
'uniform_dataset_generator', None)
if uniform_dataset_fn:
uniform_dataset = uniform_dataset_fn(
**cfgs.GENERATE_VAE_DATASET.generate_uniform_dataset_kwargs)
else:
uniform_dataset = None
observation_key = cfgs.SKEW_FIT.get('observation_key',
'latent_observation')
desired_goal_key = cfgs.SKEW_FIT.get('desired_goal_key',
'latent_desired_goal')
achieved_goal_key = desired_goal_key.replace("desired", "achieved")
obs_dim = (env.observation_space.spaces[observation_key].low.size +
env.observation_space.spaces[desired_goal_key].low.size)
action_dim = env.action_space.low.size
hidden_sizes = cfgs.Q_FUNCTION.get('hidden_sizes', [400, 300])
qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
target_qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
target_qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes,
)
policy = TanhGaussianPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=cfgs.POLICY.get('hidden_sizes', [400, 300]),
)
vae = env.vae
replay_buffer = OnlineVaeRelabelingBuffer(
vae=env.vae,
env=env,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
achieved_goal_key=achieved_goal_key,
priority_function_kwargs=cfgs.PRIORITY_FUNCTION,
**cfgs.REPLAY_BUFFER)
vae_trainer = ConvVAETrainer(
cfgs.VAE_TRAINER.train_data,
cfgs.VAE_TRAINER.test_data,
env.vae,
beta=cfgs.VAE_TRAINER.beta,
lr=cfgs.VAE_TRAINER.lr,
)
# assert 'vae_training_schedule' not in cfgs, "Just put it in algo_kwargs"
max_path_length = cfgs.SKEW_FIT.max_path_length
trainer = SACTrainer(env=env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**cfgs.TWIN_SAC_TRAINER)
trainer = HERTrainer(trainer)
eval_path_collector = VAEWrappedEnvPathCollector(
cfgs.SKEW_FIT.evaluation_goal_sampling_mode,
env,
MakeDeterministic(policy),
decode_goals=True, # TODO check this
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
expl_path_collector = VAEWrappedEnvPathCollector(
cfgs.SKEW_FIT.exploration_goal_sampling_mode,
env,
policy,
decode_goals=True,
observation_key=observation_key,
desired_goal_key=desired_goal_key,
)
algorithm = OnlineVaeAlgorithm(
trainer=trainer,
exploration_env=env,
evaluation_env=env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
vae=vae,
vae_trainer=vae_trainer,
uniform_dataset=uniform_dataset, # TODO used in test vae
max_path_length=max_path_length,
parallel_vae_train=cfgs.VAE_TRAINER.parallel_train,
**cfgs.ALGORITHM)
if cfgs.SKEW_FIT.custom_goal_sampler == 'replay_buffer':
env.custom_goal_sampler = replay_buffer.sample_buffer_goals
algorithm.to(ptu.device)
vae.to(ptu.device)
algorithm.train()