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name: recurrent_impala_proxy description: Run a tiny recurrent policy proxy with an optimizer step that mirrors NLE baseline training evidence.

Recurrent Impala Proxy

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

1. Confirm the input contract before computing downstream values.
2. Preserve the paper mechanism named in the module document rather than copying repository layout.
3. Use the script in scripts/ for deterministic behavior and record outputs in recovery logs.
4. 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.