kartiandClaude Opus 5
4147de0282
The bridge becomes a runnable taskset. The crux is that these are continuous control environments and the agent emits text: the model issues a control decision that is held for N steps, and the hold length is measured rather than guessed. At one decision every 32 steps the scripted baseline returns -2.807 and reaches the waypoint 0% of the time, against 5.484 and 100% at every frame. Open loop — one decision for the whole flight — returns -3.764 against an inaction floor of -4.161. A bird that sets a course and leaves does no better than one that does nothing, which is why crow-nav carries the strongest rule-4 claim of the five spatial environments. The budget bites from both ends: 8 turns reach the goal 44% of the time, 12 reach 88%, 14 reach 100%. 32 episodes against Qwen3.8-27B: mean 0.4603, no malformed replies in 412, every episode replaying to an identical checksum, and 16 of 32 spending all 16 turns. The gate fires 43.75% of the time and separates the four scenarios cleanly — whatever is wrong with the four dead data-environment gates, it is not that binary gates cannot discriminate. The oracle comes from the worker's own op. Nothing recomputes in Python a number that came out of TypeScript, least of all the reward denominator. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
19 lines
667 B
Python
19 lines
667 B
Python
"""tera-crow-nav — the first Tera spatial taskset, over the NDJSON bridge.
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The taskset id resolves to this module name, so both plugin classes are exported
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from here: the `Taskset` the run loads its tasks from, and the `Env` that programs
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the flight loop host-side. `TeraSpatialEnv` lives in `tera_spatial` because the
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loop is the same for every spatial environment; what makes this one crow-nav is
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`CrowNavTaskset`.
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"""
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from tera_crow_nav.taskset import CrowNavConfig, CrowNavTaskset
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from tera_spatial.spatial import TeraSpatialEnv, TeraSpatialEnvConfig
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__all__ = [
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"CrowNavConfig",
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"CrowNavTaskset",
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"TeraSpatialEnv",
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"TeraSpatialEnvConfig",
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]
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