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ARXIV:2603.26647 · REINFORCEMENT LEARNING · SUBMITTED 30 MAR · 22:29 UTC · FRESHNESS STALE
ARXIV:2603.26647REINFORCEMENT LEARNINGSUBMITTED 30 MAR · 22:29 UTCFRESHNESS STALEAshutosh Soni · Peizhong Ju · Atilla Eryilmaz · Ness B. Shroff · arXiv
A novel policy for multi-armed bandits that optimizes exploration in dynamic environments with side-observations and stochastic availability.
Opportunity summary
Pain A novel policy for multi-armed bandits that optimizes exploration in dynamic environments with side-observations and stochastic availability.
Evidence 22 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A novel policy for multi-armed bandits that optimizes exploration in dynamic environments with side-observations and stochastic availability. We use a bipartite graph to link actions to a set of unknowns, such that selecting an…
We study the stochastic multi-armed bandit (MAB) problem where an underlying network structure enables side-observations across related actions. We use a bipartite graph to link actions to a set of unknowns, such that selecting…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We study the stochastic multi-armed bandit (MAB) problem where an underlying network structure enables side-observations across related actions. Code availability is flagged in the…
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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Score4.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A novel policy for multi-armed bandits that optimizes exploration in dynamic environments with side-observations and stochastic availability.
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Paper Pack
10.48550/arXiv.2603.26647A novel policy for multi-armed bandits that optimizes exploration in dynamic environments with side-observations and stochastic availability.
Abstract
We study the stochastic multi-armed bandit (MAB) problem where an underlying network structure enables side-observations across related actions. We use a bipartite graph to link actions to a set of unknowns, such that selecting an action reveals observations for all the unknowns it is connected to. While previous works rely on the assumption that all actions are permanently accessible, we investigate the more practical setting of stochastic availability, where the set of feasible actions (the "activation set") varies dynamically in each round. This framework models real-world systems with both structural dependencies and volatility, such as social networks where users provide side-information about their peers' preferences, yet are not always online to be queried. To address this challenge, we propose UCB-LP-A, a novel policy that leverages a Linear Programming (LP) approach to optimize exploration-exploitation trade-offs under stochastic availability. Unlike standard network bandit algorithms that assume constant access, UCB-LP-A computes an optimal sampling distribution over the realizable activation sets, ensuring that the necessary observations are gathered using only the currently active arms. We derive a theoretical upper bound on the regret of our policy, characterizing the impact of both the network structure and the activation probabilities. Finally, we demonstrate through numerical simulations that UCB-LP-A significantly outperforms existing heuristics that ignore either the side-information or the availability constraints.
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Proof status
unverified22 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Commercial
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Dimensions overall score 4.0
PROBLEM
A novel policy for multi-armed bandits that optimizes exploration in dynamic environments with side-observations and stochastic availability. We use a bipartite graph to link actions to a set of unknowns, such that selecting an action reveals observations for all the unknowns it...
METHOD
We study the stochastic multi-armed bandit (MAB) problem where an underlying network structure enables side-observations across related actions. We use a bipartite graph to link actions to a set of unknowns, such that selecting an action reveals observations for all the unknowns...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We study the stochastic multi-armed bandit (MAB) problem where an underlying network structure enables side-observations across related actions. Code availability is flagged in the production record; the...
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
UCB-LP-A, a novel policy that leverages a Linear Programming (LP) approach to optimize exploration-exploitation trade-offs under stochastic availability.
The abstract explicitly states the novelty and the core methodology of the proposed policy.
partial
UCB-LP-A computes an optimal sampling distribution over the realizable activation sets, ensuring that the necessary observations are gathered using only the currently active arms.
The abstract clearly describes how the policy addresses the challenge of stochastic availability.
partial
We derive a theoretical upper bound on the regret of our policy, characterizing the impact of both the network structure and the activation probabilities.
The abstract explicitly mentions the derivation of a theoretical regret bound and its dependencies.
partial
Finally, we demonstrate through numerical simulations that UCB-LP-A significantly outperforms existing heuristics that ignore either the side-information or the availability constraints.
The abstract concludes with a strong statement about the performance of the proposed policy compared to existing methods.
partial
This framework models real-world systems with both structural dependencies and volatility, such as social networks where users provide side-information about their peers' preferences, yet are not always online to be queried.
The abstract provides a clear motivation and real-world analogy for the problem formulation.
partial
The objective function minimizes the total expected sampling frequency. The primary constraint imposes a global observability requirement, ensuring that every base-armiaccumu-lates at least unit mass of observation across all activation sets.
The description of the LP problem in the abstract and the subsequent section clearly outlines its objective and constraints.
partial
This demonstrates that information gain is not strictly bound by an arm’s instantaneous avail-ability. By strategically leveraging the network topology and weigning the occurrence probabilitiesp, our policy efficiently accumulates observations for specific arms through these side-channels, even when those arms are effectively ‘offline’.
The analysis excerpt clearly explains how side-observations overcome the limitation of stochastic availability.
partial
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Concepts
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Materials
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A novel policy for multi-armed bandits that optimizes exploration in dynamic environments with side-observations and stochastic availability.
Segment
Reinforcement Learning
Adoption evidence
No public code link in the paper record yet
Commercial read
4.0/10 public viability
Direct
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Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Evidence coverage
OpportunityKernel evidence_receipt
22 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
22 references, 3 sources, 50% evidence coverage.
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Buyer clarity
missing
Current read
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Defensibility
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Defensibility signals are missing.
Evidence
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Regulatory load
missing
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No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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Build Passport does not name an implementer.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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BUZZ
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