Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error

Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/pseudo-quantized-actor-critic-algorithm-for-robustness-to-noisy-temporal-difference-error

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Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

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 pseudo-quantized-actor-critic-algorithm-for-robustness-to-noisy-temporal-difference-error | Route /signal-canvas/pseudo-quantized-actor-critic-algorithm-for-robustness-to-noisy-temporal-difference-error

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/pseudo-quantized-actor-critic-algorithm-for-robustness-to-noisy-temporal-difference-error

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error

PDF: https://arxiv.org/pdf/2604.01613v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.820Z

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Not build-ready: Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error

/buildability/pseudo-quantized-actor-critic-algorithm-for-robustness-to-noisy-temporal-difference-error

Ignoreblocked

Subject: Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error

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
this paper revisits the TD learning algorithm based on control as inference, deriving a novel algorithm capable of robust learning against noisy TD errors
Implicationpartial
Directly stated in the title and abstract as the main contribution of the paper
Verificationpartial
partial
Evidencepartial
when the sigmoid function saturates with a large TD error probably due to noise, the gradient vanishes, implicitly excluding it from learning
Implicationpartial
Directly described in the abstract as a key mechanism of the proposed method
Verificationpartial
partial
Evidencepartial
the two divergences exhibit distinct gradient-vanishing characteristics
Implicationpartial
Directly stated in the abstract as an analytical finding
Verificationpartial
partial
Evidencepartial
the optimality is decomposed into multiple levels to achieve pseudo-quantization of TD errors, aiming for further noise reduction
Implicationpartial
Directly stated in the abstract as a specific technical approach
Verificationpartial
partial
Evidencepartial
a Jensen-Shannon divergence-based approach is approximately derived to inherit the characteristics of both divergences
Implicationpartial
Directly stated in the abstract as an extension of the method
Verificationpartial
partial
Evidencepartial
These benefits are verified through RL benchmarks, demonstrating stable learning even when heuristics are insufficient or rewards contain noise
Implicationpartial
Directly stated in the abstract with verification through benchmarks
Verificationpartial
partial
Evidencepartial
they cause side effects like increased computational cost and reduced learning efficiency
Implicationpartial
Directly stated in the abstract as motivation for the new approach
Verificationpartial
partial
Evidencepartial
since the TD error is defined by a bootstrap method, its computation tends to be noisy and destabilize learning
Implicationpartial
Directly stated in the abstract as a fundamental problem in RL
Verificationpartial
partial

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Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error

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