from typing import Any, List from ray.rllib.connectors.connector import ( ConnectorContext, AgentConnector, register_connector, ) from ray.rllib.models.preprocessors import get_preprocessor from ray.rllib.policy.sample_batch import SampleBatch from ray.rllib.utils.annotations import DeveloperAPI from ray.rllib.utils.typing import AgentConnectorDataType # Bridging between current obs preprocessors and connector. # We should not introduce any new preprocessors. # TODO(jungong) : migrate and implement preprocessor library in Connector framework. @DeveloperAPI class ObsPreprocessorConnector(AgentConnector): """A connector that wraps around existing RLlib observation preprocessors. This includes: - OneHotPreprocessor for Discrete and Multi-Discrete spaces. - GenericPixelPreprocessor and AtariRamPreprocessor for Atari spaces. - TupleFlatteningPreprocessor and DictFlatteningPreprocessor for flattening arbitrary nested input observations. - RepeatedValuesPreprocessor for padding observations from RLlib Repeated observation space. """ def __init__(self, ctx: ConnectorContext): super().__init__(ctx) self._preprocessor = get_preprocessor(ctx.observation_space)( ctx.observation_space, ctx.config.get("model", {}) ) def __call__(self, ac_data: AgentConnectorDataType) -> List[AgentConnectorDataType]: d = ac_data.data assert ( type(d) == dict ), "Single agent data must be of type Dict[str, TensorStructType]" if SampleBatch.OBS in d: d[SampleBatch.OBS] = self._preprocessor.transform(d[SampleBatch.OBS]) if SampleBatch.NEXT_OBS in d: d[SampleBatch.NEXT_OBS] = self._preprocessor.transform( d[SampleBatch.NEXT_OBS] ) return [ac_data] def to_config(self): return ObsPreprocessorConnector.__name__, {} @staticmethod def from_config(ctx: ConnectorContext, params: List[Any]): return ObsPreprocessorConnector(ctx, **params) register_connector(ObsPreprocessorConnector.__name__, ObsPreprocessorConnector)