# Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems

arXiv: https://arxiv.org/abs/1607.07762 · HTML: https://mlsanity.com/paper/1607.07762/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2016.

## Built on by (most cited first, 5 of 5)

- [Pre-trained Gaussian Processes for Bayesian Optimization](https://mlsanity.com/paper/2109.08215/) (2021)
- [QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?](https://mlsanity.com/paper/2503.22674/) (2025)
- [Grammar Prompting for Domain-Specific Language Generation with Large Language Models](https://mlsanity.com/paper/2305.19234/) (2023)
- [Pre-training helps Bayesian optimization too](https://mlsanity.com/paper/2207.03084/) (2022)
- [HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes](https://mlsanity.com/paper/2212.10538/) (2022)

## Abstract (arXiv)

We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) the planner focuses on the most relevant states to the current planning problem; and (3) the planner focuses on the most informative and/or high-value actions. Our theoretical analysis shows the validity and asymptotic optimality of the proposed approach. Empirically, we demonstrate the effectiveness of our algorithm on a simulated multi-modal pushing problem.