Q&A 30.4 — DriveRegulator

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Q&A: Phase 30.4 — DriveRegulator

Q1: Why a tolerance band instead of always correcting toward the setpoint?

A: Constant correction creates oscillation — the system would spend most of its time making tiny adjustments rather than pursuing goals. The tolerance band (e.g., target=0.5 ± 0.1) defines a "good enough" zone where no regulatory action is needed. This follows Hull's (1943) drive reduction model and biological homeostasis: organisms don't maintain exact temperature/hunger levels, they maintain ranges.

Q2: How does the hybrid exploration strategy avoid mode collapse?

A: Each of the three strategies has different failure modes: ε-greedy can explore too randomly, UCB can over-explore uncertain-but-low-value goals, Thompson can lock onto early winners. By combining all three via weighted average, the system inherits the strengths of each: UCB's principled uncertainty handling, Thompson's Bayesian adaptivity, and ε-greedy's simplicity as a baseline floor.

Q3: What happens when the resource budget is exhausted?

A: Goals that cannot be allocated any resources are added to blocked_goals in the RegulationAction. The AutonomyOrchestrator (30.5) skips blocked goals during action selection. The FrustrationTolerance module tracks how many cycles a goal has been blocked — if it exceeds the escalation threshold, the goal is decomposed or abandoned.

Q4: Can the homeostatic setpoints be learned rather than hand-configured?

A: Currently, setpoints are configured at initialization. However, the MetaCognitiveMonitor (Phase 29.3) tracks long-term drive satisfaction patterns. A future extension could use this data to learn optimal setpoints via gradient-free optimization — adjusting setpoints to maximize long-term task performance. The frozen dataclass design makes this a clean swap.

Q5: How does the frustration mechanism avoid premature goal abandonment?

A: The 10-attempt threshold before abandonment is deliberately high, and escalation is gradual: retry → decompose → deprioritize → abandon. Most goals resolve at the decomposition stage (attempts 4-6) because the original goal was too coarse. The frustration counter resets to 0 on abandonment and on any successful sub-goal completion.

Q6: Is the exploration rate global or per-goal?

A: The exploration_rate in RegulationAction is global — it determines the probability that the AutonomyOrchestrator selects a non-top-ranked goal. However, UCB and Thompson scores are computed per-goal, so the exploration signal is informed by individual goal uncertainty. High uncertainty in a specific goal increases the chance it's selected during exploration.