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name: torch-agent-training description: "Route and compose public single-agent PyTorch agents, models, memories, resources, trainers, and experiment controls in skrl 2.1.0." disable-model-invocation: true metadata: disco-role: operating license: MIT

Torch agent training

Use this route for a single-agent PyTorch skrl 2.1.0 workflow: choose an algorithm, build its model-role dictionary, allocate rollout/replay memory, configure preprocessors/noises/schedulers, select a trainer, and make a safe train/evaluate/resume plan. Start with the bounded component check in scripts/torch_ppo_components.py, then use the references for the exact role keys and construction details.

Route quickly

1. Establish the observation, optional state, and action spaces through the environment route; this skill does not choose or implement Gymnasium wrappers. See ../../references/framework-selection.md and ../../references/troubleshooting.md in the root skill before committing to a backend or trainer.
2. Choose an algorithm and its required model roles in references/algorithm-and-component-selection.md. Match the policy distribution to the action space and use a deterministic value/critic model where the table requires it.
3. Compose the models, RandomMemory (when required), configuration, and agent using references/workflow-and-api.md. Keep memory_size and the agent's rollout/update settings consistent.
4. Use SequentialTrainer for the normal loop, StepTrainer when the caller owns each iteration, and ParallelTrainer only after checking its process, pickling, shared-memory, and hardware constraints.
5. Disable writes during a smoke/evaluation probe, load a checkpoint into an architecture-equivalent agent, and only then enable training or logging. Check workflow-specific failure modes in references/troubleshooting.md.

Boundaries

• Environment wrappers and space conversion: route to the environment-integration sibling.
• IPPO/MAPPO, multi-agent trainers, and Runner/YAML: route to ../../sub-skills/multi-agent-and-runner/SKILL.md.
• JAX and Warp: not covered here.
• Framework-wide backend selection and package-wide install/import diagnosis belong to the root route; this sub-skill owns Torch-specific composition.

Completion checklist

Before handing a workflow to a researcher, verify: torch and skrl import; all model dictionary keys are exact; each model's input/output role is compatible with the space; the memory device and environment count agree; the trainer config has finite, intentional timesteps; logging/checkpoint paths are explicitly chosen; and resume/evaluation uses an architecture-compatible agent. A CPU smoke establishes import and CPU behavior only—it does not prove CUDA or another accelerator.