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name: collectors-and-replay description: "Collect rollouts, select collector topologies, configure replay buffers, and debug TorchRL data movement." disable-model-invocation: true metadata: disco-role: operating license: MIT

TorchRL collectors and replay

Use this sub-skill when a task involves TorchRL rollout collection, evaluation rollouts, replay-buffer composition, sequence or slice sampling, prioritized replay, generation-safe record updates, memmap storage, checkpointing, HER, or optional service-backed replay.

Route here for

• Collector, AsyncCollector, MultiCollector, Evaluator, frames_per_batch, total_frames, trajs_per_batch, sync, backend, backend_options, num_collectors, update_policy_weights_, and weight-update schemes.
• Direct, local process, Ray, RPC, distributed, and Submitit collector topology selection.
• ReplayBuffer, TensorDictReplayBuffer, TensorDictPrioritizedReplayBuffer, LazyTensorStorage, LazyMemmapStorage, SliceSampler, Sequence, PrioritizedSampler, TensorDictRoundRobinWriter, update_tensordict_priority, and update_if_present.
• Collector-to-replay integration, explicit device / storing_device / policy_device / env_device decisions, and debugging TensorDict movement across collection and storage.

Route out instead

• Environment construction, specs, transforms, Gym/Gymnasium wrappers, and step_mdp layout questions: use envs-and-transforms.
• Actor, critic, recurrent policy, distribution, and TensorDictModule construction: use modules-and-policies.
• Loss modules, trainers, target updates, and algorithm optimization loops: use objectives-and-training.
• Generic TorchRL service registry, LLM collectors, VLA schemas, rendering, or serving backends: use llm-vla-and-services.

Operating references

1. Read collector-workflows.md for collector topology, sync/async choices, explicit devices, Evaluator, and collector-to-replay patterns.
2. Read replay-buffer-workflows.md for buffer composition, storages, samplers, writers, transforms, sequence units, priorities, HER, memmap, checkpointing, and service-backed replay.
3. Read api-reference.md for verified signatures and import paths.
4. Read troubleshooting.md when collection, storage, sampling, priority updates, process workers, memmap cleanup, or optional Ray services fail.

Bundled smoke helpers

• scripts/smoke_collector.py: CPU-only direct collector, explicit devices, direct collector-to-replay integration, and synchronous Evaluator smoke.
• scripts/smoke_replay_buffer.py: CPU-only TensorDict replay, LazyTensor/LazyMemmap storage, prioritized replay, Sequence sample unit masks, checkpoint round trip, and generation-safe updates.

Run these helpers from any working directory after TorchRL and TensorDict are importable. They do not download assets, launch distributed services, or train models.

Evidence basis

This runtime guidance was distilled from relative TorchRL evidence covering torchrl/collectors/, torchrl/data/replay_buffers/, collector and replay reference pages, collector/evaluator/replay tutorials, selected collector and replay examples, selected test/collectors/ and test/rb/ behavior, and installed API probes. Optional Ray, RPC, Submitit, CUDA, simulator, and service-backed behaviors are documented as optional or reference-only unless a future task verifies those backends explicitly.