Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes

Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 69
Source count: 3
Coverage: 67%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID optimistic-actor-critic-with-parametric-policies-for-linear-markov-decision-processes | Route /signal-canvas/optimistic-actor-critic-with-parametric-policies-for-linear-markov-decision-processes

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: 69

Proof: Verification pending

Freshness state: computing

Source paper: Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes

PDF: https://arxiv.org/pdf/2603.28595v1

Source count: 3

Coverage: 67%

Last proof check: 2026-03-31T20:30:20.275Z

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Not build-ready: Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes

/buildability/optimistic-actor-critic-with-parametric-policies-for-linear-markov-decision-processes

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Subject: Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
We prove that the resulting algorithm achieves Õ(ε⁻⁴) and Õ(ε⁻²) sample complexity in the on-policy and off-policy setting, respectively.
Implicationpartial
Explicitly stated in the abstract with theoretical proof provided in the paper.
Verificationpartial
partial
Evidencepartial
We prove that the resulting algorithm achieves Õ(ε⁻⁴) and Õ(ε⁻²) sample complexity in the on-policy and off-policy setting, respectively.
Implicationpartial
Explicitly stated in the abstract with theoretical proof provided in the paper.
Verificationpartial
partial
Evidencepartial
Such policies are computationally expensive to sample from, making the environment interactions inefficient. To that end, we focus on the finite-horizon linear MDPs and propose an optimistic actor-critic framework that uses parametric log-linear policies.
Implicationpartial
Directly stated in the abstract and introduction as a motivation for the work, contrasting with prior methods.
Verificationpartial
partial
Evidencepartial
Consequently, we instead choose Proj to minimize the following regression loss in the logit space
Implicationpartial
Explicitly described as the chosen method in the algorithm description.
Verificationpartial
partial
Evidencepartial
For the critic, we use approximate Thompson sampling via Langevin Monte Carlo to obtain optimistic value estimates.
Implicationpartial
Directly stated in the abstract and detailed in the algorithm description.
Verificationpartial
partial
Evidencepartial
The following lemma shows that LMC can offer similar guarantees [to UCB bonuses].
Implicationpartial
Supported by Lemma 5.1 which provides formal guarantees for the critic's optimism.
Verificationpartial
partial
Evidencepartial
These results are only meaningful if the mismatch ratio is bounded. However, a bounded mismatch ratio indicates that the initial state distribution already provides a good coverage over the state space, thereby sidestepping the exploration problem.
Implicationpartial
Direct critique presented in the analysis section of the paper.
Verificationpartial
partial
Evidencepartial
Our results match prior theoretical works in achieving the state-of-the-art sample complexity, while our algorithm is more aligned with practice.
Implicationpartial
Directly claimed in the abstract, supported by the use of parametric policies and tractable objectives.
Verificationpartial
partial

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Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes

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