The World-Model Qualification Test › Key Parameters

The key parameters for the world-model qualification experiments are:

• DT: Timestep of the discretized plant. It sets the scale of one-step displacement and appears in both the true dynamics and the fitted models.
• DAMPING: Damping coefficient $c$ of the cart. Stronger damping makes velocity decay faster, shrinking the benefit of a history-based state.
• OBS_NOISE: Standard deviation of sensor noise. Together with fresh process disturbance, it contributes to a lower-bound reference for one-step prediction error.
• DIST_STD: Standard deviation of the random disturbance force. It keeps the regulation task non-trivial; without it, zero control would be optimal.
• lam: Ridge regularization strength in fit_ridge. It stabilizes the closed-form solve when features are highly correlated.
• HORIZON: Number of steps in the recursive rollouts used to measure compounding error.
• PLAN_HORIZON: Lookahead horizon of the random-shooting MPC planner.
• N_CANDIDATES: Number of candidate action sequences sampled at each replanning step.
• EPISODE_STEPS and N_EPISODES: Length and count of the closed-loop evaluation episodes, which together set the noise level of the cost comparison.

Several of these parameters repay deliberate manipulation once you have the notebook running, because each one tunes a different lesson. Raising DAMPING toward large values makes velocity die quickly, so recent position alone approximates the full state and the memoryless model's handicap shrinks; lowering it does the opposite and widens the gap. Increasing OBS_NOISE raises the floor beneath both models and makes the history window's averaging role more valuable. Reducing N_CANDIDATES or PLAN_HORIZON degrades the planner uniformly, illustrating that planner quality and model quality are separable axes of the closed-loop result. Experiments like these convert the parameter list from documentation into a small laboratory.