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https://github.com/vale981/ray
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60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
from typing import Any, List
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from ray.rllib.connectors.connector import (
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ConnectorContext,
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AgentConnector,
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register_connector,
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)
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from ray.rllib.models.preprocessors import get_preprocessor
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.utils.annotations import DeveloperAPI
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from ray.rllib.utils.typing import AgentConnectorDataType
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# Bridging between current obs preprocessors and connector.
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# We should not introduce any new preprocessors.
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# TODO(jungong) : migrate and implement preprocessor library in Connector framework.
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@DeveloperAPI
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class ObsPreprocessorConnector(AgentConnector):
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"""A connector that wraps around existing RLlib observation preprocessors.
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This includes:
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- OneHotPreprocessor for Discrete and Multi-Discrete spaces.
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- GenericPixelPreprocessor and AtariRamPreprocessor for Atari spaces.
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- TupleFlatteningPreprocessor and DictFlatteningPreprocessor for flattening
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arbitrary nested input observations.
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- RepeatedValuesPreprocessor for padding observations from RLlib Repeated
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observation space.
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"""
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def __init__(self, ctx: ConnectorContext):
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super().__init__(ctx)
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self._preprocessor = get_preprocessor(ctx.observation_space)(
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ctx.observation_space, ctx.config.get("model", {})
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)
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def __call__(self, ac_data: AgentConnectorDataType) -> List[AgentConnectorDataType]:
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d = ac_data.data
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assert (
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type(d) == dict
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), "Single agent data must be of type Dict[str, TensorStructType]"
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if SampleBatch.OBS in d:
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d[SampleBatch.OBS] = self._preprocessor.transform(d[SampleBatch.OBS])
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if SampleBatch.NEXT_OBS in d:
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d[SampleBatch.NEXT_OBS] = self._preprocessor.transform(
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d[SampleBatch.NEXT_OBS]
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)
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return [ac_data]
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def to_config(self):
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return ObsPreprocessorConnector.__name__, {}
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@staticmethod
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def from_config(ctx: ConnectorContext, params: List[Any]):
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return ObsPreprocessorConnector(ctx, **params)
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register_connector(ObsPreprocessorConnector.__name__, ObsPreprocessorConnector)
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