Imported from yzj623/pplab (
ResearchAgent-Skill/skill_library/paperbench/ftrl/skill/nethack_learning_environment/action_task_protocol/SKILL.md). Install upstream withnpx skills add yzj623/pplab --skill action_task_protocol. Copyright stays with the author.
Action Task Protocol
When To Use
Use this skill when reconstructing or testing the NetHack Learning Environment paper mechanisms in a bounded recovery setting. It is appropriate for symbolic NLE-style observations, task/action contracts, exploration bonuses, trainable baseline proxies, or validation harnesses depending on the module name.
Inputs
- JSON-compatible records produced by upstream NLE recovery modules.
- Tiny deterministic fixtures for smoke tests or reduced recovery.
- Paper-derived target metadata when the module contributes to recovery evidence.
Outputs
- Compact Python dictionaries or scalar values that can be serialized into Distiller recovery logs.
- Explicit errors for malformed input instead of silent fallback behavior.
Workflow
- Confirm the input contract before computing downstream values.
- Preserve the paper mechanism named in the module document rather than copying repository layout.
- Use the script in
scripts/for deterministic behavior and record outputs in recovery logs. - During recovery, do not read the original NLE repository; rely on paper/module/skill artifacts only.
Validation
Run python -m pytest tests from this skill directory, or validate through the Distiller validate_skill_tree.py --run-tests command. The tests use only the Python standard library and deterministic fixtures.
Limitations
This skill supports reduced/proxy recovery. It does not claim to reproduce full distributed IMPALA training or a compiled NetHack simulator by itself.