---

name: "training-integration" description: "Plan, validate, and hand off optional MyoSuite policy evaluation or RL training integrations without starting uncontrolled experiments." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MyoSuite Training Integration

Use this sub-skill when a task involves an optional RL framework, a saved policy, an experiment configuration, or a reproducible training handoff for a MyoSuite environment. Keep environment use separate from experiment execution: the base CPU package can create and step MyoSuite environments without any RL framework, scheduler, model download, external account, JAX, or CUDA stack.

This sub-skill owns planning, dependency/config validation, bounded evaluation, callback contracts, and safety gates. It does not own long-running training, benchmark execution, scheduler submission, credential setup, or model download.

Route The Request

Request	Route
Check whether optional training packages are present	Run scripts/check_training_deps.py.
Evaluate a local saved policy	Follow Policy evaluation.
Understand SB3, MJRL, DEP-RL, or TorchRL boundaries	Read Framework boundaries.
Validate or design an experiment config	Read Configuration.
Map Hydra launch concepts or callbacks	Read Launch and callback contracts.
Diagnose a missing package, checkpoint, or space mismatch	Read Troubleshooting.
Start a full run, benchmark, Slurm job, or download	Stop at a reproducible handoff; require explicit execution outside this skill.

Establish The Contract

Before loading a policy or drafting a run, collect:

1. Exact environment id and MyoSuite/package versions.
2. Framework and algorithm (stable_baselines3.PPO, SAC, MJRL NPG, DEP-RL, TorchRL, or a custom policy).
3. Policy artifact type, local path, trusted origin, and companion state such as VecNormalize statistics.
4. Observation and action spaces used during training.
5. Evaluation budget: seed, episodes, maximum steps, render mode, and output directory.
6. For training: config snapshot, dependency lock, hardware/backend, resource cap, checkpoint cadence, evaluation protocol, and stop conditions.

Do not guess a framework from a suffix alone. A .zip commonly denotes an SB3 model, .pickle often denotes an MJRL-style object, and DEP-RL usually loads a run directory, but provenance and loader API remain authoritative.

Preflight Without Training

From this sub-skill directory, run a metadata-only check:

python scripts/check_training_deps.py --framework sb3 --json

Validate a local artifact without deserializing it:

python scripts/check_training_deps.py \
  --framework sb3 \
  --policy ./policy.zip \
  --json

Validate a project-owned YAML configuration without invoking Hydra or a launcher:

python scripts/check_training_deps.py \
  --framework sb3 \
  --launcher hydra-local \
  --config ./experiment.yaml \
  --json

The script performs no installation, download, deserialization, training, scheduler submission, credential lookup, or output creation. Optional --probe-env --env <id> creates and resets one environment, reports spaces, and closes it; it never steps or trains. Exit 0 means requested checks passed and the config is within the script's agent-session guardrail, 1 means a missing or invalid requirement, 2 is CLI misuse, and 3 means the config parsed but exceeds or evades the bounded-run guardrail. None of these statuses authorizes training.

Evaluate A Saved Policy Safely

Use the loader that produced the artifact:

• SB3: PPO.load(path, env=env, device="cpu") or SAC.load(...), then model.predict(obs, deterministic=True).
• MJRL/examine-style policy: trusted pickle only; the object must implement get_action(obs), whose first returned item is the action.
• DEP-RL: deprl.load(path, env) for a user run, or deprl.load_baseline(env) only when baseline acquisition is explicitly permitted.
• Custom: define and record an adapter with one clear action method and validate its output against env.action_space before stepping.

Do not pass an SB3 .zip directly to myosuite.utils.examine_env. Its policy contract is a module-qualified class constructible as (env, seed) or a trusted pickled object with get_action; SB3 uses load plus predict. Use a bounded custom evaluation loop for SB3. See training workflows.

Always check the package before the file loader, check file existence before loading, and check spaces before rollout. This preserves distinct diagnoses for ModuleNotFoundError, FileNotFoundError, and action/observation-space errors. Never deserialize an untrusted pickle or checkpoint.

Training Safety Gate

When asked to launch a full run from an agent session:

1. Do not call model.learn, a historical launcher, a shell training helper, Hydra multirun, Submitit, Slurm, a benchmark, or a download command.
2. Produce a resolved config and run manifest using the fields in configuration.
3. Run only dependency, file, and config checks; use the optional reset-only probe only if asset availability must be confirmed.
4. Mark requested side effects: working directory, files, videos, checkpoints, network services, credentials, worker count, RAM, GPU, and wall time.
5. Hand back a command template for human-controlled execution, not an executed command. Require explicit approval and an execution context with monitoring, cancellation, quotas, and cleanup.

A tiny learning smoke is still training and may create logs or initialize accelerators. Treat it as an explicit verification candidate, not a default preflight.

Backend Boundary

• Base CPU: MyoSuite, MuJoCo, Gymnasium, and CPU policy evaluation. SB3 can run on CPU when installed; set device="cpu" for a portable handoff.
• Optional learner GPU: PyTorch frameworks may use CUDA for neural-network computation. This does not convert standard MyoSuite Gym environments into MJX environments or prove simulator acceleration.
• Optional MJX/JAX: MyoSuite's MJX environments require their separate JAX, MJX, and related dependency set. CUDA adds another JAX backend requirement. Never infer MJX/CUDA readiness from a base reset/step or from torch.cuda.is_available().

For MJX/JAX planning, route to the sibling MJX acceleration sub-skill as well. Do not use benchmark-scale PPO scripts as a training template here.

Completion Criteria

A training-integration handoff is complete only when it states:

• loader/framework, trusted artifact, environment id, and space compatibility;
• dependency and config check results, with missing optional packages explicit;
• bounded evaluation or training budget and deterministic settings;
• model, normalization, config, and version artifacts to preserve;
• side effects, credentials, hardware, and scheduler assumptions;
• exact human-controlled launch concept and stop/recovery procedure;
• unresolved gaps, especially untested CUDA/MJX or unavailable policy files.