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Opponent State Inference Under Partial Observability: An HMM-POMDP Framework for 2026 Formula 1 Energy Strategy

Kalliopi Kleisarchaki

TL;DR

The counter-harvest trap is formally characterised -- a deceptive strategy in which a car deliberately suppresses observable deployment signals to induce a rival into a failed attack -- and it is shown that detecting it requires belief-state inference rather than reactive threshold rules.

Abstract

The 2026 Formula 1 technical regulations introduce a fundamental change to energy strategy: under a 50/50 internal combustion engine / battery power split with unlimited regeneration and a driver-controlled Override Mode (abbreviated MOM throughout), the optimal energy deployment policy depends not only on a driver's own state but on the hidden state of rival cars. This creates a Partially Observable Stochastic Game that cannot be solved by single-agent optimisation methods. We present a tractable two-layer inference and decision framework. The first layer is a 30-state Hidden Markov Model (HMM) that infers a probability distribution over each rival's ERS charge level, Override Mode status, and tyre degradation state from five publicly observable telemetry signals. The second layer is a Deep Q-Network (DQN) policy that takes the HMM belief state as input and selects between energy deployment strategies. We formally characterise the counter-harvest trap -- a deceptive strategy in which a car deliberately suppresses observable deployment signals to induce a rival into a failed attack -- and show that detecting it requires belief-state inference rather than reactive threshold rules. On synthetic races generated from the model's own assumptions, the HMM achieves 92.3% ERS inference accuracy (random baseline: 33.3%) and detects counter-harvest trap conditions with 95.7% recall. Pre-registration -- empirical validation begins Australian Grand Prix, 8 March 2026.

Opponent State Inference Under Partial Observability: An HMM-POMDP Framework for 2026 Formula 1 Energy Strategy

TL;DR

The counter-harvest trap is formally characterised -- a deceptive strategy in which a car deliberately suppresses observable deployment signals to induce a rival into a failed attack -- and it is shown that detecting it requires belief-state inference rather than reactive threshold rules.

Abstract

The 2026 Formula 1 technical regulations introduce a fundamental change to energy strategy: under a 50/50 internal combustion engine / battery power split with unlimited regeneration and a driver-controlled Override Mode (abbreviated MOM throughout), the optimal energy deployment policy depends not only on a driver's own state but on the hidden state of rival cars. This creates a Partially Observable Stochastic Game that cannot be solved by single-agent optimisation methods. We present a tractable two-layer inference and decision framework. The first layer is a 30-state Hidden Markov Model (HMM) that infers a probability distribution over each rival's ERS charge level, Override Mode status, and tyre degradation state from five publicly observable telemetry signals. The second layer is a Deep Q-Network (DQN) policy that takes the HMM belief state as input and selects between energy deployment strategies. We formally characterise the counter-harvest trap -- a deceptive strategy in which a car deliberately suppresses observable deployment signals to induce a rival into a failed attack -- and show that detecting it requires belief-state inference rather than reactive threshold rules. On synthetic races generated from the model's own assumptions, the HMM achieves 92.3% ERS inference accuracy (random baseline: 33.3%) and detects counter-harvest trap conditions with 95.7% recall. Pre-registration -- empirical validation begins Australian Grand Prix, 8 March 2026.
Paper Structure (77 sections, 1 theorem, 11 equations, 9 tables)

This paper contains 77 sections, 1 theorem, 11 equations, 9 tables.

Table of Contents

  1. Introduction
  2. Scope and limitations.
  3. Background
  4. POMDPs and opponent modelling.
  5. HMMs for temporal inference.
  6. HMM identifiability.
  7. Deep Q-Networks.
  8. Problem Formalisation
  9. The 2026 Regulatory Context
  10. 50/50 power split.
  11. Override Mode.
  12. Active Aero.
  13. Circuit-dependent recharge shortfall.
  14. The POMDP Model
  15. Fuel exclusion.
  16. ...and 62 more sections

Key Result

Proposition 1

The counter-harvest trap is not detectable by a single-observable threshold policy on $\Delta v_\text{trap}$ alone.

Theorems & Definitions (9)

  • Definition 1: Fully Observable State
  • Definition 2: Hidden Rival State
  • Definition 3: Action Space
  • Definition 4: Observation Space
  • Definition 5: Belief State
  • Definition 6: Reward
  • Definition 7: Counter-Harvest Trap
  • Proposition 1
  • proof : Proof sketch