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name: objectives-and-training description: "Choose and wire TorchRL losses, value estimators, target updaters, trainers, Hydra configs, and SOTA recipes." disable-model-invocation: true metadata: disco-role: operating license: MIT

TorchRL objectives and training

Use this sub-skill when a task is about TorchRL objective modules, value estimators, target-network updates, trainer hooks, Hydra algorithm configs, or choosing an algorithm recipe. Typical triggers: ClipPPOLoss, PPOLoss, SACLoss, DQNLoss, DDPGLoss, TD3Loss, IQLLoss, CQLLoss, MAPPOLoss, ValueEstimators, make_value_estimator, set_keys, _AcceptedKeys, SoftUpdate, HardUpdate, Trainer, trainer hooks, config parity, and sota-implementations.

Route before acting

• Actor, critic, distribution, recurrent-module, and policy-wrapper construction belongs to sibling sub-skill modules-and-policies. Return here after the modules expose the TensorDict keys the loss expects.
• Collector topology, replay buffers, prioritized samplers, and data movement belong to sibling sub-skill collectors-and-replay. Return here for loss-side priority keys and target updates.
• Environment specs, action specs, step_mdp, simulator setup, and transforms belong to sibling sub-skill envs-and-transforms.
• LLM GRPO/SFT/RLHF and VLA-specific serving or dataset workflows belong to sibling sub-skill llm-vla-and-services; this sub-skill only covers generic loss-key and trainer conventions that those workflows share.

Operating sequence

1. Pick the loss family from loss API reference by algorithm, action space, online/offline setting, and multi-agent layout.
2. Verify the actor/critic/replay/env dependencies before writing training code: module out_keys, environment action_spec, replay sample keys, and ("next", ...) transition fields must match the loss key map.
3. Configure TensorDict keys with loss.set_keys(...) using the accepted key names listed in loss API reference. Prefer NestedKey tuples for nested fields.
4. Build the value estimator with loss.make_value_estimator(ValueEstimators.<name>, ...) or a standalone estimator such as GAE; make sure reward/done/terminated keys are forwarded to the estimator.
5. Add target-network updaters when the loss has delayed target parameters, and call updater step() at the correct cadence; see training algorithms.
6. If using TorchRL Trainer, register hooks deliberately and follow config/class parity rules from trainer and configs.
7. Use SOTA implementation map as a reference-only recipe index. Do not run long SOTA launchers as generic helper scripts.
8. For failures, start with troubleshooting, then use the bundled inspection helper.

Bundled helper

• scripts/inspect_loss_keys.py prints a named loss class signature, whether set_keys is available, default configurable TensorDict keys when accessible, and the default value estimator. It performs no training and is safe to run from any current directory.

Verification boundary

The skill content is based on repository source, public reference docs, tutorials, SOTA recipe files, objective tests, trainer/config code, and CPU package/API inspection. Core objective/trainer APIs are CPU-verifiable. CUDA kernels, Triton paths, distributed learner backends, simulator-heavy examples, and long SOTA training are optional or reference-only unless a future task provisions those dependencies explicitly.