mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
from typing import Any, List
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from ray.rllib.connectors.connector import (
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AgentConnector,
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Connector,
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ConnectorContext,
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ConnectorPipeline,
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get_connector,
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register_connector,
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)
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from ray.rllib.utils.typing import ActionConnectorDataType, AgentConnectorDataType
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from ray.util.annotations import PublicAPI
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@PublicAPI(stability="alpha")
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class AgentConnectorPipeline(ConnectorPipeline, AgentConnector):
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def __init__(self, ctx: ConnectorContext, connectors: List[Connector]):
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super().__init__(ctx)
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self.connectors = connectors
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def is_training(self, is_training: bool):
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self._is_training = is_training
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for c in self.connectors:
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c.is_training(is_training)
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def reset(self, env_id: str):
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for c in self.connectors:
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c.reset(env_id)
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def on_policy_output(self, output: ActionConnectorDataType):
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for c in self.connectors:
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c.on_policy_output(output)
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def __call__(
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self, acd_list: List[AgentConnectorDataType]
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) -> List[AgentConnectorDataType]:
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ret = acd_list
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for c in self.connectors:
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ret = c(ret)
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return ret
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def to_config(self):
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return AgentConnectorPipeline.__name__, [c.to_config() for c in self.connectors]
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@staticmethod
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def from_config(ctx: ConnectorContext, params: List[Any]):
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assert (
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type(params) == list
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), "AgentConnectorPipeline takes a list of connector params."
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connectors = [get_connector(ctx, name, subparams) for name, subparams in params]
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return AgentConnectorPipeline(ctx, connectors)
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register_connector(AgentConnectorPipeline.__name__, AgentConnectorPipeline)
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