Sequential stock-control policy review project

Freeze the process definition

Define the stock unit, decision interval, state snapshot, legal actions, transition event, reward timing and genuine terminal event. Include outstanding orders if they affect the next state. The state contract is the minimum record for both planning and learning.

Build a small baseline model

Estimate transitions and immediate rewards from historical episodes with their policy versions. Solve the finite planning model and compare its proposed actions with the reviewed existing policy. Validate one-step probabilities and longer simulated trajectories on held-out periods. Value iteration only certifies the calculation for that model.

Check learning and logged support

Calculate mature episode returns and inspect terminal handling. If Q-learning is considered, report safe exploration, state-action visits and stability across seeds. Screen every proposed action and trajectory against historical support. Returns, Q updates and support each answer different questions.

Assess business consequences

Report shortages, service value, labor cost, capacity incidents and group-level effects across future periods. An attractive simulated discounted return is insufficient if rare shortages are omitted or safety rules are violated. Any prospective test needs assignment logs, stop rules and an approved rollback. Exploration controls carry over from one-step policies.

Keep the decision bounded

The code checks that the review packet exists; it does not establish model accuracy, safe exploration or expected improvement. Attach measured results, uncertainty and the responsible owner before considering a pilot. A missing state or support check keeps the release on hold.

Implementation

def sequential_review_gate(packet):
    requirements = {
        "state_contract_signed": "state contract",
        "reward_clock_audited": "reward clock",
        "terminal_rules_tested": "terminal rules",
        "transition_model_checked": "transition model",
        "trajectory_support_checked": "trajectory support",
        "safety_actions_enforced": "safety actions",
        "rollback_owner_named": "rollback owner",
    }
    missing = [label for field, label in requirements.items() if not packet.get(field)]
    return "eligible for pilot review" if not missing else "hold: " + ", ".join(missing)

stock_packet = {
    "state_contract_signed": True, "reward_clock_audited": True,
    "terminal_rules_tested": True, "transition_model_checked": False,
    "trajectory_support_checked": False, "safety_actions_enforced": True,
    "rollback_owner_named": True,
}
assert sequential_review_gate(stock_packet) == (
    "hold: transition model, trajectory support"
)

Performance and operating cost

The gate is O(K) for K checks. Finite value iteration costs O(JSAO) for J sweeps, S states, A actions and O outcomes per action; trajectory audits scan logged decisions. The main operating costs are safe evidence collection, rare-event validation, model monitoring and a credible rollback path.

Common Mistakes

• Do not replace business safety constraints with a weak reward penalty.
• Do not infer a long-run candidate value from missing state-action trajectories.
• Do not call an optimal solution to an unchecked simulator a release-ready policy.

Read next

• Sequential decision state and reward contract
• Bellman value iteration for stock control
• Episode returns, temporal difference and terminal states
• Q-learning control and exploration
• Offline trajectory support and simulator risk
• Contextual bandit policy release review project

Continue the workflow: Model compression release project.