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name: sequential_replay_protocol description: Model P3O iteration ordering by appending current rollouts before bounded sequential replay mini-batch updates.

Sequential Replay Protocol

Use this skill to preserve Algorithm 1 ordering: collect current trajectories, update on-policy, then sample replay updates. Do not use it as a simulator or environment wrapper.

Inputs

• Existing replay buffer records.
• Fresh rollout records with stored behavior policy probabilities.
• Maximum buffer size and replay update count.

Outputs

• Updated replay buffer.
• Ordered update trace showing on-policy before off-policy replay.
• Replay mini-batches with source ids.

Workflow

1. Append fresh rollouts and trim the buffer to the configured maximum size.
2. Emit the fresh rollout ids as the on-policy update batch.
3. Select replay mini-batches deterministically for bounded recovery or via a supplied sampler in larger runs.
4. Record trace entries so analysis can verify policy-on then policy-off execution.

Source Boundary

Use this skill with the paper, module documents, generated artifacts, and ordinary package documentation. Do not read or depend on the original P3O repository.

Validation

Run python scripts/<script>.py --self-test or python -m pytest tests from the skill directory. The bundled tests use only the Python standard library.