pith:ZYGI7GDF
R2PS: Worst-Case Robust Real-Time Pursuit Strategies under Partial Observability
Belief preservation extends dynamic programming to partial observability for real-time robust pursuit policies.
arxiv:2511.17367 v2 · 2025-11-21 · cs.LG
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Claims
After reinforcement learning, our policy achieves robust zero-shot generalization to unseen real-world graph structures and consistently outperforms the policy directly trained on the test graphs by the existing game RL approach.
The belief preservation mechanism successfully extends the optimality properties of the dynamic programming strategies to the partially observable setting while preserving worst-case robustness against asynchronous evader moves.
R2PS combines a proof that dynamic programming remains optimal under asynchronous evader moves, a belief preservation mechanism for partial observability, and integration into equilibrium policy generalization to produce real-time pursuer policies that zero-shot generalize to unseen graphs.
References
Receipt and verification
| First computed | 2026-05-17T23:39:00.721826Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ce0c8f986564f8318db2b1572e1c1f5002fd766b0e0b1de7890a31a3c898dcce
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZYGI7GDFMT4DDDNSWFLS4HA7KA \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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