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name: replay-and-data description: "Use Acme adders, Reverb replay tables and datasets, offline data iterators, image augmentation, and replay shape troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Replay and Data

Use this sub-skill when an Acme task involves collecting actor experience, writing it to Reverb, reading replay samples into a learner, adapting offline demonstrations, or diagnosing replay/data structure mismatches.

Read First

• Replay API: Adder contracts, Reverb adder families, signatures, dataset helpers, and expected shapes.
• Replay workflows: choose transition/sequence/episode/structured adders, build Reverb/TFDS/numpy iterators, and connect learner data.
• Troubleshooting: optional dependency failures, Reverb table signature errors, sequence/period pitfalls, extras mismatches, and invalid offline data paths.
• Replay structure helper: validate a JSON description of observation/action/reward/discount/extras nesting and print an adder-family recommendation without importing Reverb.

Route Boundaries

• Stay here for acme.adders.base, acme.adders.wrappers, acme.adders.reverb.*, acme.datasets.reverb, acme.datasets.tfds, acme.datasets.numpy_iterator, acme.datasets.image_augmentation, and acme.utils.reverb_utils.
• Use the sibling core-workflows sub-skill for environment-loop basics, wrapper selection, dm_env conversion, experiment scaffolding, or actor/learner process layout before replay details matter.
• Use the sibling jax-agents or tf-agents sub-skill when the question is which algorithm-specific builder, config, or learner expects a particular replay table or iterator shape.

Fast Choices

• Pick NStepTransitionAdder for feed-forward learners that train on (observation, action, reward, discount, next_observation, extras) transitions.
• Pick SequenceAdder for recurrent learners, sequence losses, overlapping unrolls, or learner code that expects time-major/batched trajectories.
• Pick EpisodeAdder for full-episode imitation/offline pipelines or algorithms that need complete trajectories.
• Pick StructuredAdder when one actor stream must populate multiple tables or custom item patterns with Reverb structured_writer configs.
• For offline demonstrations, adapt data into types.Transition batches or Reverb-compatible samples, then expose an iterator matching the learner constructor.