mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
from typing import Any, Callable, List, Type
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import numpy as np
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import tree # dm_tree
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from ray.rllib.connectors.connector import (
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AgentConnector,
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ConnectorContext,
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register_connector,
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)
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.utils.typing import (
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AgentConnectorDataType,
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AgentConnectorsOutput,
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)
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from ray.util.annotations import PublicAPI
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@PublicAPI(stability="alpha")
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def register_lambda_agent_connector(
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name: str, fn: Callable[[Any], Any]
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) -> Type[AgentConnector]:
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"""A util to register any simple transforming function as an AgentConnector
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The only requirement is that fn should take a single data object and return
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a single data object.
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Args:
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name: Name of the resulting actor connector.
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fn: The function that transforms env / agent data.
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Returns:
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A new AgentConnector class that transforms data using fn.
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"""
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class LambdaAgentConnector(AgentConnector):
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def transform(self, ac_data: AgentConnectorDataType) -> AgentConnectorDataType:
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return AgentConnectorDataType(
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ac_data.env_id, ac_data.agent_id, fn(ac_data.data)
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)
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def to_config(self):
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return name, None
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@staticmethod
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def from_config(ctx: ConnectorContext, params: List[Any]):
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return LambdaAgentConnector(ctx)
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LambdaAgentConnector.__name__ = name
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LambdaAgentConnector.__qualname__ = name
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register_connector(name, LambdaAgentConnector)
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return LambdaAgentConnector
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@PublicAPI(stability="alpha")
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def flatten_data(data: AgentConnectorsOutput):
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assert isinstance(
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data, AgentConnectorsOutput
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), "Single agent data must be of type AgentConnectorsOutput"
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for_training = data.for_training
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for_action = data.for_action
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flattened = {}
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for k, v in for_action.items():
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if k in [SampleBatch.INFOS, SampleBatch.ACTIONS] or k.startswith("state_out_"):
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# Do not flatten infos, actions, and state_out_ columns.
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flattened[k] = v
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continue
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if v is None:
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# Keep the same column shape.
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flattened[k] = None
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continue
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flattened[k] = np.array(tree.flatten(v))
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flattened = SampleBatch(flattened, is_training=False)
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return AgentConnectorsOutput(for_training, flattened)
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# Agent connector to build and return a flattened observation SampleBatch
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# in addition to the original input dict.
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FlattenDataAgentConnector = PublicAPI(stability="alpha")(
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register_lambda_agent_connector("FlattenDataAgentConnector", flatten_data)
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)
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