Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids

Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/critic-free-deep-reinforcement-learning-for-maritime-coverage-path-planning-on-irregular-hexagonal-grids

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 81
Source count: 3
Coverage: 50%

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Canonical ID critic-free-deep-reinforcement-learning-for-maritime-coverage-path-planning-on-irregular-hexagonal-grids | Route /signal-canvas/critic-free-deep-reinforcement-learning-for-maritime-coverage-path-planning-on-irregular-hexagonal-grids

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Evidence Receipt

Route status: building

Claims: 8

References: 81

Proof: Verification pending

Freshness state: computing

Source paper: Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids

PDF: https://arxiv.org/pdf/2603.28385v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-31T20:18:15.428Z

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Watch and verify: Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids

/buildability/critic-free-deep-reinforcement-learning-for-maritime-coverage-path-planning-on-irregular-hexagonal-grids

Watchwatch

Subject: Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
To overcome the instability of value estimation in long-horizon routing problems, we implement a critic-free Group-Relative Policy Optimization (GRPO) scheme. This method estimates advantages through within-instance comparisons of sampled trajectories rather than relying on a value function.
Implicationpartial
The method is explicitly named and its mechanism is directly described in the abstract and analysis.
Verificationpartial
partial
Evidencepartial
Experiments on 1,000 unseen synthetic maritime environments demonstrate that a trained policy achieves a 99.0% Hamiltonian success rate, more than double the best heuristic (46.0%)
Implicationpartial
Specific numeric results are directly stated in the abstract with a clear comparison to a baseline.
Verificationpartial
partial
Evidencepartial
while producing paths 7% shorter and with 24% fewer heading changes than the closest baseline.
Implicationpartial
Specific numeric improvements in path quality metrics are directly stated in the abstract.
Verificationpartial
partial
Evidencepartial
All three inference modes (greedy, stochastic sampling, and sampling with 2-opt refinement) operate under 50~ms per instance on a laptop GPU, confirming feasibility for real-time on-board deployment.
Implicationpartial
Performance latency is explicitly stated with a clear conclusion about real-time feasibility.
Verificationpartial
partial
Evidencepartial
Hexagonal tessellations address this limitation: they minimize overlap and provide better isotropy than square grids, making motion costs direction-agnostic
Implicationpartial
The advantage of hexagonal grids is directly stated as addressing a limitation of traditional methods, though the evidence quote is from a problem description section.
Verificationpartial
partial
Evidencepartial
All three RL variants achieve zero revisits on every solved instance, confirming that the action-masking mechanism (Section 3.6) enforces strict Hamiltonian feasibility by construction.
Implicationpartial
The result of the mechanism (zero revisits) is explicitly stated in the analysis, and the mechanism itself is referenced.
Verificationpartial
partial
Evidencepartial
Traditional Coverage Path Planning (CPP) approaches depend on decomposition techniques that struggle with irregular coastlines, islands, and exclusion zones
Implicationpartial
The limitation of traditional methods is directly stated in the abstract, though it is a general characterization rather than a specific measured result.
Verificationpartial
partial
Evidencepartial
Exact decompositions are attractive when one can exploit geometric structure, but they become cumbersome as the AOI grows in complexity (multiple holes, narrow passages) and when additional operational constraints must be incorporated
Implicationpartial
The limitation is explicitly described in the problem formulation/related works section, though it is a general statement not tied to a specific experiment.
Verificationpartial
partial

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Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids

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