d3rlpy.models.SimBaEncoderFactory

class d3rlpy.models.SimBaEncoderFactory(feature_size=256, hidden_size=1024, n_blocks=1)

SimBa encoder factory class.

This class implements SimBa encoder architecture.

References

• Lee et al., SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning,

Parameters:

• feature_size (int) – Feature unit size.

• hidden_size (int) – HIdden expansion layer unit size.

• n_blocks (int) – Number of SimBa blocks.

Methods

create(observation_shape)

Returns PyTorch’s state enocder module.

Parameters:

observation_shape (Union[Sequence[int], Sequence[Sequence[int]]]) – observation shape.

Returns:

an enocder object.

Return type:

SimBaEncoder

create_with_action(observation_shape, action_size, discrete_action=False)

Returns PyTorch’s state-action enocder module.

Parameters:

• observation_shape (Union[Sequence[int], Sequence[Sequence[int]]]) – observation shape.

• action_size (int) – action size. If None, the encoder does not take action as input.

• discrete_action (bool) – flag if action-space is discrete.

Returns:

an enocder object.

Return type:

SimBaEncoderWithAction

classmethod deserialize(serialized_config)

Parameters:

serialized_config (str) –

Return type:

TConfig

classmethod deserialize_from_dict(dict_config)

Parameters:

dict_config (dict[str, Any]) –

Return type:

TConfig

classmethod deserialize_from_file(path)

Parameters:

path (str) –

Return type:

TConfig

classmethod from_dict(kvs, *, infer_missing=False)

Parameters:

kvs (Optional[Union[dict, list, str, int, float, bool]]) –

Return type:

A

classmethod from_json(s, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw)

Parameters:

s (Union[str, bytes, bytearray]) –

Return type:

A

static get_type()

Return type:

str

classmethod schema(*, infer_missing=False, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)

Parameters:

• infer_missing (bool) –

• many (bool) –

• partial (bool) –

Return type:

SchemaF[A]

serialize()

Return type:

str

serialize_to_dict()

Return type:

dict[str, Any]

to_dict(encode_json=False)

Return type:

Dict[str, Optional[Union[dict, list, str, int, float, bool]]]

to_json(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, indent=None, separators=None, default=None, sort_keys=False, **kw)

Parameters:

• skipkeys (bool) –

• ensure_ascii (bool) –

• check_circular (bool) –

• allow_nan (bool) –

• indent (Optional[Union[int, str]]) –

• separators (Optional[Tuple[str, str]]) –

• default (Optional[Callable]) –

• sort_keys (bool) –

Return type:

str

Attributes

feature_size: int = 256

hidden_size: int = 1024

n_blocks: int = 1