Analytic Planning under Uncertainty with Moment Closure
Effective model-based reinforcement learning in stochastic environments requires planning that accounts for predictive uncertainty. Propagating full state distributions analytically offers a principled way to do this, but has traditionally required restrictive policy or reward structures to remain tractable. Consequently, modern deep reinforcement learning has largely retreated to either stochastic sampling, which introduces significant target variance, or deterministic point estimates that igno
Record details
Published: 3 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Environment
Retrieved: 4 August 2026
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ethics.ai (3 August 2026), “Analytic Planning under Uncertainty with Moment Closure,” evidence record 16087, https://ethics.ai/record/16087 (originally published by arXiv cs.AI).
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