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.algos import CQL
from d3rlpy.dataset import MDPDataset

dataset = MDPDataset(...)

# choose from ['pixel', 'min_max', 'standard'] or None
cql = CQL(scaler='standard')

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

# 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 scalers by yourself.

from d3rlpy.preprocessing import StandardScaler

scaler = StandardScaler(mean=..., std=...)

cql = CQL(scaler=scaler)

`d3rlpy.preprocessing.PixelScaler`	Pixel normalization preprocessing.
`d3rlpy.preprocessing.MinMaxScaler`	Min-Max normalization preprocessing.
`d3rlpy.preprocessing.StandardScaler`	Standardization preprocessing.

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.algos import CQL
from d3rlpy.dataset import MDPDataset

dataset = MDPDataset(...)

# 'min_max' or None
cql = CQL(action_scaler='min_max')

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

# 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 = CQL(action_scaler=action_scaler)

d3rlpy.preprocessing.MinMaxActionScaler

Min-Max normalization action preprocessing.

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.algos import CQL
from d3rlpy.dataset import MDPDataset

dataset = MDPDataset(...)

# 'min_max', 'standard' or None
cql = CQL(reward_scaler='standard')

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

# 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 = CQL(reward_scaler=reward_scaler)

# ClipRewardScaler is the only option you need to initialize manually
reward_scaler = ClipRewardScaler(-1.0, 1.0)
cql = CQL(reward_scaler=reward_scaler)

`d3rlpy.preprocessing.MinMaxRewardScaler`	Min-Max reward normalization preprocessing.
`d3rlpy.preprocessing.StandardRewardScaler`	Reward standardization preprocessing.
`d3rlpy.preprocessing.ClipRewardScaler`	Reward clipping preprocessing.