preprocessing.rst

Preprocessing

d3rlpy.preprocessing

Observation

d3rlpy provides several preprocessors tightly incorporated with algorithms. Each preprocessor is implemented with PyTorch operation, which will be included in the model exported by save_policy method.

from d3rlpy.datasets import get_pendulum
from d3rlpy.algos import CQLConfig
from d3rlpy.preprocesing import StandardObservationScaler

dataset, _ = get_pendulum()

# choose from ['pixel', 'min_max', 'standard'] or None
cql = CQLConfig(observation_scaler=StandardObservationScaler()).create()

# observation scaler is fitted from the given dataset
cql.fit(dataset, n_steps=100000)

# preprocesing is included in TorchScript
cql.save_policy('policy.pt')

# you don't need to take care of preprocessing at production
policy = torch.jit.load('policy.pt')
action = policy(unpreprocessed_x)

You can also initialize observation scalers by yourself.

from d3rlpy.preprocessing import StandardObservationScaler

observation_scaler = StandardObservationScaler(mean=..., std=...)

cql = CQLConfig(observation_scaler=observation_scaler).create()

d3rlpy.preprocessing.PixelObservationScaler d3rlpy.preprocessing.MinMaxObservationScaler d3rlpy.preprocessing.StandardObservationScaler

Action

d3rlpy also provides the feature that preprocesses continuous action. With this preprocessing, you don't need to normalize actions in advance or implement normalization in the environment side.

from d3rlpy.datasets import get_pendulum
from d3rlpy.algos import CQLConfig
from d3rlpy.preprocessing import MinMaxActionScaler

dataset, _ = get_pendulum()

cql = CQLConfig(action_scaler=MinMaxActionScaler()).create()

# action scaler is fitted from the given episodes
cql.fit(dataset, n_steps=100000)

# postprocessing is included in TorchScript
cql.save_policy('policy.pt')

# you don't need to take care of postprocessing at production
policy = torch.jit.load('policy.pt')
action = policy(x)

You can also initialize scalers by yourself.

from d3rlpy.preprocessing import MinMaxActionScaler

action_scaler = MinMaxActionScaler(minimum=..., maximum=...)

cql = CQLConfig(action_scaler=action_scaler).create()

d3rlpy.preprocessing.MinMaxActionScaler

Reward

d3rlpy also provides the feature that preprocesses rewards. With this preprocessing, you don't need to normalize rewards in advance. Note that this preprocessor should be fitted with the dataset. Afterwards you can use it with online training.

from d3rlpy.datasets import get_pendulum
from d3rlpy.algos import CQLConfig
from d3rlpy.preprocessing import StandardRewardScaler

dataset, _ = get_pendulum()

cql = CQLConfig(reward_scaler=StandardRewardScaler()).create()

# reward scaler is fitted from the given episodes
cql.fit(dataset)

# reward scaler is also available at finetuning.
cql.fit_online(env)

You can also initialize scalers by yourself.

from d3rlpy.preprocessing import MinMaxRewardScaler

reward_scaler = MinMaxRewardScaler(minimum=..., maximum=...)

cql = CQLConfig(reward_scaler=reward_scaler).create()

d3rlpy.preprocessing.MinMaxRewardScaler d3rlpy.preprocessing.StandardRewardScaler d3rlpy.preprocessing.ClipRewardScaler d3rlpy.preprocessing.MultiplyRewardScaler d3rlpy.preprocessing.ReturnBasedRewardScaler d3rlpy.preprocessing.ConstantShiftRewardScaler