XiuchengWu
300e86956b
- Add ConsensusSnapshot and ConsensusSlot classes for structured consensus state representation. - Introduce OnlineLanguageLearner for stateful online learning with experience replay and EWC regularization. - Create WorkspaceEvent and ActionEvent classes for serializable event types. - Develop InMemoryVectorMemoryStore for storing and querying workspace events. - Implement ActionPolicy to manage action selection and motor parameterization. - Establish a unified WorkspaceRuntime for managing the agent's lifecycle and interactions. - Enhance tests in test_smoke.py to cover new functionalities and ensure correctness.
129 lines
3.7 KiB
Python
129 lines
3.7 KiB
Python
"""
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Serializable event types for the workspace-centered runtime.
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These data packets are intentionally small and plain. They give local modules,
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future worker processes, and external transports the same language for passing
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workspace state around without sharing live Python objects.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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import time
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from typing import Any
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import numpy as np
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import torch
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from .consensus import ConsensusSnapshot
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def _workspace_vector(w: torch.Tensor | np.ndarray | list[float]) -> list[float]:
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if isinstance(w, torch.Tensor):
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data = w.detach().cpu()
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if data.dim() > 1:
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data = data[0]
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return data.reshape(-1).tolist()
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if isinstance(w, np.ndarray):
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data = w
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if data.ndim > 1:
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data = data[0]
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return data.reshape(-1).astype(float).tolist()
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return [float(v) for v in w]
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def _consensus_dict(consensus: ConsensusSnapshot | dict | None) -> dict | None:
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if consensus is None:
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return None
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if isinstance(consensus, ConsensusSnapshot):
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return consensus.to_dict()
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return consensus
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@dataclass
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class WorkspaceEvent:
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"""A transport-friendly snapshot of workspace state."""
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step: int
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modality: str
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workspace: list[float]
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consensus: dict | None = None
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context: dict[str, Any] = field(default_factory=dict)
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timestamp: float = field(default_factory=time.time)
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kind: str = "workspace"
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@classmethod
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def from_tensor(
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cls,
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w: torch.Tensor | np.ndarray | list[float],
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modality: str,
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step: int = 0,
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consensus: ConsensusSnapshot | dict | None = None,
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context: dict | None = None,
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) -> "WorkspaceEvent":
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return cls(
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step=step,
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modality=modality,
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workspace=_workspace_vector(w),
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consensus=_consensus_dict(consensus),
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context=context or {},
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)
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def workspace_array(self) -> np.ndarray:
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return np.asarray(self.workspace, dtype=np.float32)
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def to_dict(self) -> dict:
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return {
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"kind": self.kind,
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"step": self.step,
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"modality": self.modality,
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"workspace": list(self.workspace),
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"consensus": self.consensus,
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"context": self.context,
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"timestamp": self.timestamp,
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}
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@dataclass
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class ActionEvent:
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"""A transport-friendly action decision/result."""
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step: int
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action_idx: int
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action_name: str
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action_params: list[float]
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executed: bool
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risk: float = 0.0
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context: dict[str, Any] = field(default_factory=dict)
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timestamp: float = field(default_factory=time.time)
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kind: str = "action"
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@classmethod
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def from_action_info(
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cls,
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action_info: dict,
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step: int = 0,
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context: dict | None = None,
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) -> "ActionEvent":
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return cls(
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step=step,
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action_idx=int(action_info.get("action_idx", 0)),
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action_name=str(action_info.get("action_name", "observe")),
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action_params=[float(v) for v in action_info.get("action_params", [])],
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executed=bool(action_info.get("executed", False)),
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risk=float(action_info.get("risk", 0.0)),
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context=context or {},
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)
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def to_dict(self) -> dict:
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return {
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"kind": self.kind,
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"step": self.step,
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"action_idx": self.action_idx,
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"action_name": self.action_name,
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"action_params": list(self.action_params),
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"executed": self.executed,
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"risk": self.risk,
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"context": self.context,
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"timestamp": self.timestamp,
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}
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