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---
name: go_explore_return_then_explore_loop
description: Execute a bounded Go-Explore return-then-explore Phase 1 loop in resettable sparse-reward environments.
---
# Go-Explore Return Then Explore Loop
Use this skill to run the central Go-Explore Phase 1 mechanism after cell encoding and archive update rules are available. It is appropriate for resettable or replayable environments where a selected archive state can be restored before exploratory actions are taken.
## Inputs
- A resettable environment exposing state snapshots, actions, and rewards.
- Cell encoder and archive update/select functions.
- Iteration count, rollout horizon, action schedule, and random seed.
## Outputs
- Expanded archive.
- Best trajectory, score, and goal flag.
- Trace proving selected cells were restored before exploration.
## Workflow
1. Insert the start state into the archive.
2. Select a cell from the archive with seeded deterministic selection.
3. Restore the saved state for that cell without exploratory actions.
4. Execute exploratory actions for a bounded horizon.
5. Encode each visited state and update the archive using score and length rules.
6. Repeat until the iteration budget ends or the goal is reached.
## Validation
Run `python tests/test_return_loop.py` or validate with `validate_skill_tree.py --run-tests`.
## Limitations
This skill is a bounded recovery implementation, not an Atari emulator. It is faithful to the reset/archive/explore mechanism but does not perform neural robustification.
