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name: continuous-control description: "Guides minimalRL DDPG, continuous PPO, and SAC workflows for Pendulum-style continuous-action control." disable-model-invocation: true metadata: disco-role: operating license: MIT

Continuous-Control Workflows

Use this sub-skill when a task asks about minimalRL's continuous-action Pendulum algorithms: DDPG, PPO-Continuous, or SAC.

Route by task

• Deterministic actor-critic / DDPG: read references/api-reference.md for MuNet, QNet, replay, OU noise, and soft_update; read references/workflows.md for action scaling and target updates.
• Continuous PPO: use references/workflows.md for Gaussian rollout/minibatch handling and references/troubleshooting.md for mu, std, and log-prob shape issues.
• Soft Actor-Critic: use references/api-reference.md for PolicyNet, twin QNet, calc_target, and alpha update contracts.
• Pre-training shape checks: run scripts/smoke_continuous_control.py.

Do not use this sub-skill for

• Discrete CartPole REINFORCE, actor-critic, PPO, PPO-LSTM, or V-trace policy/value routes; use ../on-policy-discrete/SKILL.md.
• DQN, ACER, or V-trace replay/correction debugging; use ../off-policy-value/SKILL.md.
• A2C/A3C multiprocessing; use ../parallel-actor-critic/SKILL.md.

Quick operating workflow

1. Confirm the environment has continuous actions. The minimalRL continuous scripts assume Pendulum-like observations of length 3 and a one-dimensional action in [-2, 2].

2. Choose the algorithm family:

   • DDPG for deterministic actor plus critic, replay, OU exploration, and soft target updates.
   • Continuous PPO for on-policy Gaussian policy with clipped objective.
   • SAC for stochastic tanh-Gaussian policy, twin critics, entropy temperature update, and replay.

3. Read references/api-reference.md before changing tensor shapes or action scaling.

4. Run the smoke helper:

   python sub-skills/continuous-control/scripts/smoke_continuous_control.py --algorithm all
   

5. For action range, log_prob, state/action concatenation, replay warm-up, or target update failures, read references/troubleshooting.md.

Bundled runtime files

• references/api-reference.md lists source-faithful classes, functions, hyperparameters, tensor/action shapes, and data tuple contracts.
• references/workflows.md explains DDPG, continuous PPO, and SAC adaptation workflows.
• references/troubleshooting.md gives actionable fixes for continuous-control failure modes.
• scripts/smoke_continuous_control.py validates representative continuous-control tensors without importing the original repository or running training.