Parametric Resonance in Primal-Dual Learning for Slowly Non-Stationary Constrained MDPs
Organizations: Antvoice, Paris, France
Abstract
Primal-dual methods for constrained Markov decision processes are analyzed under a two-timescale assumption, with non-stationarity entering through two channels. In the additive channel, where the constraint level oscillates, the dual oscillation saturates at a floor set by the moving optimum and is amplified linearly by the timescale ratio. In the parametric channel, where the constraint sensitivity oscillates, a coherent modulation carries no resonant forcing at any depth: the dual update's stiffness modulation cancels the primal update's damping modulation. Instability instead comes from a mismatch between the constraint estimate used by the primal update and the measurement used by the dual update, producing a parametric resonance at twice the pair's natural frequency. The tolerated mismatch grows with the square root of the forcing bandwidth and collapses with the square root of the dual-to-primal step-size ratio. These results hold on the tangent linear-quadratic reduction around an active saddle, for one constraint and for constraints under a common modulation and an isotropy condition. Driving both updates from the same estimate removes the resonant forcing but introduces a stationary offset equal to the estimator's bias. In a Lagrangian soft actor-critic agent, the multiplier locks onto the sub-harmonic at the timescale ratios its identified linear loop predicts and a coherent cost critic suppresses the lock-in, while a proportional (PID) term, predicted to leave the lock-in in place, suppresses most of it through the projection of the multiplier.
Figures & tables
| implementation | policy | multiplier | |
|---|---|---|---|
| openai/safety-starter-agents , PPO-Lagrangian | |||
| PKU-Alignment/omnisafe , PPOLag | |||
| PKU-Alignment/Safe-Policy-Optimization , single agent |
| predicted | measured | |||
| 0 | 0 | 0 | ||
| 0 | 0.15 | 0.15 | ||
| 0 | 0.20 | 0.20 | ||
| 0.3 | 0 | |||
| 0.2 | 0.5 | 0.30 | ||
| 0 | 0.90 | 0.90 |
| measured threshold | |||||
|---|---|---|---|---|---|
| real spectrum | none ( ) | n/a | n/a | ||
| real spectrum | none ( ) | n/a | n/a | ||
| 0.008644 | 0.5079 | none ( ) | 2.3503 | 2.0315 | |
| 0.008674 | 0.5026 | 1.9673 | 2.3176 | 2.0104 | |
| 0.008704 | 0.4975 | 1.9484 | 2.2863 | 1.9899 | |
| 0.009992 | 0.045020 | 0.1802 | 0.18022 | 0.18008 |
Appendix figures & tables13 assets
Supplementary material from the paper’s appendix.
Appendix
| policy parameter, tangent coordinate | frequency of the non-stationarity | ||
| multiplier of the active constraint | amplitude of the additive forcing | ||
| primal step size | , | natural frequency, continuous and exact | |
| dual step size | damping rate of the pair | ||
| timescale ratio | reduced damping | ||
| constraint estimate, consumed by the primal | modulation depth seen by the primal | ||
| constraint measurement, consumed by the dual | modulation depth seen by the dual |
| 0 | 0.5 | 2 | 8 | 32 | |
|---|---|---|---|---|---|
| threshold (RMS) | 0.1264 | 0.1382 | 0.1590 | 0.2310 | 0.4262 |
| 0.99 | 1.08 | 1.25 | 1.81 | 3.34 | |
| deduced | n/a | 0.352 | 0.279 | 0.286 | 0.318 |
| (discrete) | (per step) | ||
| threshold (peak) | threshold (r.m.s.) |
| 1 | 0.500 | 0.294 | 1.47 | 1.468 | ||
| 10 | 0.157 | 2.118 | 1.06 | 1.049 | ||
| 100 | 0.045 | 22.322 | 1.12 | 1.005 | ||
| 10 | 0.143 | 2.320 | 1.16 | 1.049 | ||
| 0.1 | 1.648 | 0.200 | 1.00 | n/a |
| measured threshold | predicted | ratio | ||
|---|---|---|---|---|
| 0.01801 | 0.01800 | 1.00 | ||
| 0.05694 | 0.05693 | 1.00 | ||
| 0.1801 | 0.1802 | 1.00 | ||
| 0.5678 | 0.5764 | 0.99 | ||
| out of range | 2.075 | n/a |
| bracket for | exponent past threshold (per step) | ||
|---|---|---|---|
| – | – | ||
| 0 (single line) | ||
|---|---|---|
| 1 | ||
| 4 | ||
| 16 |
| single line | band | predicted band | ||||
|---|---|---|---|---|---|---|
| 100 | 10 | 0.1277 | 0.2570 | 0.2576 | ||
| 1000 | 100 | 0.04222 | 0.2393 | 0.2273 |
| Floquet exponent | (unprojected) | (projected) | final | % clipped | |||
|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 1 | 0 | 0.0 | ||
| 0.3 | 0.3 | 4.663 | 4.168 | 2.780 | 26.7 | ||
| 0.9 | 0.9 | 11.21 | – | – | – | ||
| 0 | 0.3 | 3.933 | 2.685 | 28.0 | |||
| 0.3 | 0 | – | – | – | |||
| 0.2 | 0.5 | 5.669 | 3.649 | 36.6 |
| 0.018 | 0.022 | 0.026 | 0.030 | 0.036 | ||
| 0.71 | 0.87 | 1.03 | 1.19 | 1.43 | ||
| locked seeds | Lagrangian | 5/10 | 7/10 | 10/10 | 6/10 | 1/10 |
| PID | 0/10 | 1/10 | 0/10 | 1/10 | 0/10 | |
| coherent | 0/5 | 0/5 | 0/5 | 0/5 | 0/5 | |
| PID, | – | 1/10 | 2/10 | – | – | |
| PID, | – | 1/10 | 0/10 | – | – |
| Additive control (Table 15 ) | forcing periods |
|---|---|
| Additive worst-case sweep (Table 7 ) | points log-spaced, |
| Floquet tongue-centre maxima | points on , on |
| Figure 1 grid | integer periods, – steps |
| Tongue-centre mismatch bisection | halvings |
| Existence-boundary sweep (Table 3 ) | periods log-spaced, – steps, refined by points on |
| Existence-boundary bisection | halvings, mismatch split , , |
| coupled | frozen policy | ratio | |
|---|---|---|---|
| 0.200000 | 2000.000 | 10 000 | |
| 0.199994 | 20.000 | 100 | |
| 0.199450 | 2.000 | 10 | |
| 0.159698 | 0.200 | 1.3 | |
| 0.020894 | 0.020008 | 1.0 | |
| 0.002087 | 0.002086 | 1.0 |
Markov Decision Processes
Markov decision processes model sequential choices under uncertainty, linking actions, state transitions, and rewards to planning and reinforcement learning.
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