# Stochastic Path Planning in Correlated Obstacle Fields

arXiv: https://arxiv.org/abs/2509.19559 · HTML: https://mlsanity.com/paper/2509.19559/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Distributional Reinforcement Learning with Quantile Regression](https://mlsanity.com/paper/1710.10044/) (2017)
- [(More) Efficient Reinforcement Learning via Posterior Sampling](https://mlsanity.com/paper/1306.0940/) (2013)
- [Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning](https://mlsanity.com/paper/1803.00101/) (2018)
- [Why is Posterior Sampling Better than Optimism for Reinforcement Learning?](https://mlsanity.com/paper/1607.00215/) (2016)
- [Distributional Reinforcement Learning for Efficient Exploration](https://mlsanity.com/paper/1905.06125/) (2019)
- [Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning](https://mlsanity.com/paper/2005.05420/) (2020)
- [Exploration by Distributional Reinforcement Learning](https://mlsanity.com/paper/1805.01907/) (2018)
- [Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning](https://mlsanity.com/paper/2206.02670/) (2022)
- [Gaussian Process Optimization with Mutual Information](https://mlsanity.com/paper/1311.4825/) (2013)
- [On the convergence of optimistic policy iteration for stochastic shortest path problem](https://mlsanity.com/paper/1808.08763/) (2018)
- [On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation](https://mlsanity.com/paper/2301.09709/) (2023)
- [Rollout Heuristics for Online Stochastic Contingent Planning](https://mlsanity.com/paper/2310.02345/) (2023)
- [Risk-Averse Traversal of Graphs with Stochastic and Correlated Edge Costs for Safe Global Planetary Mobility](https://mlsanity.com/paper/2505.13674/) (2025)

## Built on by (most cited first, 1 of 1)

- [Belief-Aware Multi-Agent Path Finding under Map Uncertainty](https://mlsanity.com/paper/2609.40269/) (2026)

## Abstract (arXiv)

We introduce the Stochastic Correlated Obstacle Scene (SCOS) problem, a navigation setting with spatially correlated obstacles of uncertain blockage status, realistically constrained sensors that provide noisy readings and costly disambiguation. Modeling the spatial correlation with Gaussian Random Field (GRF), we develop Bayesian belief updates that refine blockage probabilities, and use the posteriors to reduce search space for efficiency. To find the optimal traversal policy, we propose a novel two-stage learning framework. An offline phase learns a robust base policy via optimistic policy iteration augmented with information bonus to encourage exploration in informative regions, followed by an online rollout policy with periodic base updates via a Bayesian mechanism for information adaptation. This framework supports both Monte Carlo point estimation and distributional reinforcement learning (RL) to learn full cost distributions, leading to stronger uncertainty quantification. We establish theoretical benefits of correlation-aware updating and convergence property under posterior sampling. Comprehensive empirical evaluations across varying obstacle densities, sensor capabilities demonstrate consistent performance gains over baselines. This framework addresses navigation challenges in environments with adversarial interruptions or clustered natural hazards.