A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark

Amber Cassimon; Peter Hellinckx; Reinout Eyckerman; Siegfried Mercelis; Steven Latr\'e

A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2110.06742 v4 pith:W2IDZHCT submitted 2021-10-13 cs.LG

A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark

Amber Cassimon , Reinout Eyckerman , Siegfried Mercelis , Steven Latr\'e , Peter Hellinckx This is my paper

classification cs.LG

keywords problemauthorsdeepimplementationmulti-objectiveoriginaltreasurealternative

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original abstract

In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be quite basic, and not always representative of practical Multi-Objective Optimization problems. In an attempt to bring theory closer to practice, the authors propose an alternative, improved version of the DST problem, and prove that some of the properties that simplify the original DST problem no longer hold. The authors also provide a reference implementation and perform a comparison between their implementation, and other existing open-source implementations of the problem. Finally, the authors also provide a complete Pareto-front for their new DST problem.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning
cs.LG 2026-01 conditional novelty 6.0

GraphAllocBench provides a graph-based resource-allocation benchmark and two supplemental metrics that expose preference-consistency failures in multi-objective RL policies.