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However, we show through numerical experiments that although the optimal solution spaces are structurally isomorphic, the practical learning dynamics are fundamentally different. First, using the minimal local augmented state, the equivalence no longer holds when transitions are not independent. Second, operational constraints and causal credit-assignment errors in Temporal Difference (TD) algorithms induce different learning behaviors across regimes. Finally, leveraging this structural equivalence to bypass these learning challenges, we demonstrate successful multi-agent zero-shot policy transfer from OD to AD, paving the way for unified, efficient solution methods in complex delayed systems.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.04345 [cs.LG] |
| (or arXiv:2605.04345v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.04345 arXiv-issued DOI via DataCite (pending registration) |
From: Ana Bušić [view email]
[v1]
Tue, 5 May 2026 23:07:09 UTC (466 KB)
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