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name: training-and-algorithms description: "Choose and train Stable-Baselines3 algorithms, instantiate A2C/PPO/DQN/SAC/TD3/DDPG, handle action-space compatibility, and run short CPU smoke training." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training and Algorithms

Use this sub-skill when an agent needs to select an SB3 algorithm, construct a model, call learn(), tune basic rollout/replay parameters, or run a short safe training smoke test.

Route by Need

• For algorithm choice and action-space compatibility, read Algorithm Selection.
• For constructor-to-learn() workflows, short training snippets, train_freq, gradient_steps, gSDE, and action noise, read Training Workflows.
• For verified public exports, policy aliases, constructor parameters, and learn() notes, read API Reference.
• For common training failures and warning diagnosis, read Troubleshooting.
• For a minimal CPU smoke command, run scripts/train_smoke.py with --help first.

Boundaries

• Custom environment validation, check_env(), wrappers, make_vec_env(), and render-mode setup belong in environments-and-vectorization.
• Evaluation callbacks, saving/loading, model persistence, and deployment-style prediction loops belong in evaluation-and-persistence.
• Deep custom policy classes, feature extractors, network architecture design, and policy_kwargs internals belong in policies-and-customization.