Planning, Control, and Policy Evaluation › Summary

Decision evaluation concerns a composed model-planner-policy-environment system. Report its executed return under stated initial states, objectives, randomness, resource budgets, and baselines. Predictive quality alone does not certify that outcome, and one successful task does not certify faithful dynamics or general transfer.

Task return is the finite reward sum along an environment rollout. Expected return averages over initial states and, where relevant, planner and environment randomness. Sample efficiency describes achieved performance as a function of real-interaction budget, not return divided by sample count. This notebook is a fixed-budget experiment rather than a learning curve. Source training, adaptation, validation, and test transitions are distinct from synthetic model-search calls.

The horizon sweep matches $K\cdot H=640$ candidate transition evaluations per decision. Both learned and true-dynamics MPC peak at $H=5$ in this run, then decline; PD has the higher mean throughout. This is evidence about the specified search configuration, not a universal short-horizon rule or a unique model-error diagnosis. Candidate-transition counts are a proxy; latency and memory require separate measurement.

Model exploitation is a possible selection-sensitive failure in which model errors make chosen actions look spuriously valuable. Under conditionally unbiased candidate estimates, selection can create nonnegative expected optimism, with growth claims requiring additional assumptions about candidate values and errors. Our paired controller estimates are instead pessimistic, and the six-policy ranking test has agreement 1 and finite-set regret 0. These diagnostics measure discrepancies without inventing an exploitation or ranking failure. Uncertainty propagation, penalties, support constraints, shorter rollouts, terminal values, and replanning have tradeoffs, not safety guarantees.

Zero-shot and few-shot protocols must identify frozen components, target interactions, adaptation operations, and selection data. The target experiment pairs cases and candidate streams and discloses 400 adaptation transitions plus 2400 target evaluation transitions across four controllers. Frozen and both adapted MPC means are nearly identical on this run; no appreciable adaptation gain or source-pretraining benefit is demonstrated.

The experiments make the protocol inspectable: fixed cases and seeds, explicit return calculations, paired controller streams, and separate data and compute counters. They do not measure wall-clock latency, memory, confidence intervals, or population-wide transfer. Chapter 65 develops datasets, splits, benchmarks, and statistical summaries that make such comparisons reproducible beyond a single toy run.