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name: cartpole-classic-control description: "Operate DQN, A2C, and PPO CartPole-v1 training, testing, checkpoints, and smoke diagnostics for the reinforcement-learning repo." metadata: disco-role: operating disable-model-invocation: true license: MIT

CartPole Classic Control

Use this sub-skill when the task involves the repository's CartPole-v1 DQN, A2C, or PPO workflows: choosing the right algorithm script, explaining network/update behavior, running training or replay, diagnosing checkpoints, or doing a safe headless smoke check.

Do not use it for GridWorld dynamic programming or tabular control, standard Atari Breakout/Pong CNN workflows, hard-exploration Atari/RND/Go-Explore/robustification, or paper-level benchmark claims.

Fast route

• Need algorithm/API facts or update formulas? Read references/algorithm-and-api-guide.md.
• Need commands, flags, checkpoint names, or loader formats? Read references/cli-and-checkpoints.md.
• Need to debug imports, rendering, reward shaping, convergence, Gymnasium API changes, or checkpoint mismatch? Read references/troubleshooting.md.
• Need a quick verification that does not train or open a display? Run scripts/cartpole_smoke.py. It uses synthetic states and includes checkpoint-format and render-readiness diagnostics.

Operating guardrails

• Treat full CartPole training as a real run, not a smoke test. DQN can run up to 300 episodes, A2C up to 1000, and PPO up to 1500 update cycles with 1024 environment steps per update.
• Avoid --render during training unless the user explicitly wants an interactive window; it slows learning and can fail on headless hosts.
• --test also requests Gymnasium render_mode="human"; use it only when a display is available and a compatible checkpoint is present.
• The bundled smoke script is intentionally standalone and does not require Gymnasium, Pygame, the training scripts, or a display. It proves tensor/API shape and update invariants, not training convergence.
• Checkpoints are algorithm-specific: DQN and PPO save raw model state_dicts; A2C saves a dictionary with separate actor and critic entries.

Provenance labels

This sub-skill distills the CartPole workflows identified in the source material as 2-cartpole/1-dqn.py, 2-cartpole/2-a2c.py, 2-cartpole/3-ppo.py, and the shared CartPole environment helper 2-cartpole/env.py. Those names are provenance/workflow labels; the runtime guidance below is self-contained and does not require opening the original source files.