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A Lyapunov Analysis of Softmax Policy Gradient for Stochastic Bandits
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- Proof freshness
- stale
- Proof status
- unverified
- Display score
- 2/10
- Last proof check
- 2026-03-30
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 2
- Source count
- 3
- Coverage
- 50%
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A Lyapunov Analysis of Softmax Policy Gradient for Stochastic Bandits
Canonical ID a-lyapunov-analysis-of-softmax-policy-gradient-for-stochastic-bandits | Route /signal-canvas/a-lyapunov-analysis-of-softmax-policy-gradient-for-stochastic-bandits
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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/a-lyapunov-analysis-of-softmax-policy-gradient-for-stochastic-banditsMCP example
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}Preparing verified analysis
Dimensions overall score 2.0
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Claim map
- Evidencepartial
We adapt the analysis of policy gradient for continuous time k-armed stochastic bandits by Lattimore [2026] to the standard discrete time setup.
ImplicationpartialThis is the primary objective stated in the abstract and introduction.
Verificationpartialpartial
- Evidencepartial
we prove that with learning rate π=π(Ξ 2 min/(Ξ max log(π))) the regret isπ(πlog(π)log(π)/π)
ImplicationpartialThe abstract explicitly states the regret bound and the condition on the learning rate.
Verificationpartialpartial
- Evidencepartial
the regret isπ(πlog(π)log(π)/π) where π is the horizon andΞmin and Ξmax are the minimum and maximum gaps.
ImplicationpartialThis is a direct statement of the result in the abstract.
Verificationpartialpartial
- Evidencepartial
with learning rate π=π(Ξ 2 min/(Ξ max log(π)))
ImplicationpartialThis is a direct statement of the condition on the learning rate in the abstract.
Verificationpartialpartial
- Evidencepartial
A Lyapunov Analysis of Softmax Policy Gradient for Stochastic Bandits
ImplicationpartialThe title explicitly mentions 'A Lyapunov Analysis'.
Verificationpartialpartial
- Evidencepartial
There are π actions and the horizon isπ with πβ₯π
ImplicationpartialThis is a fundamental assumption of the problem setup stated in the introduction.
Verificationpartialpartial
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