# Monte Carlo Planning in Hybrid Belief POMDPs

arXiv: https://arxiv.org/abs/2211.07735 · HTML: https://mlsanity.com/paper/2211.07735/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models](https://mlsanity.com/paper/1810.11692/) (2018)
- [Adaptive Information Belief Space Planning](https://mlsanity.com/paper/2201.05673/) (2022)
- [D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints](https://mlsanity.com/paper/2202.04954/) (2022)
- [Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints](https://mlsanity.com/paper/2207.08096/) (2022)

## Built on by (most cited first, 2 of 2)

- [Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees](https://mlsanity.com/paper/2303.02139/) (2023)
- [Towards Optimal Performance and Action Consistency Guarantees in Dec-POMDPs with Inconsistent Beliefs and Limited Communication](https://mlsanity.com/paper/2512.20778/) (2025)

## Abstract (arXiv)

Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the context of planning. Moreover, existing online Partially Observable Markov Decision Processes (POMDPs) solvers do not support hybrid beliefs directly. In particular, these solvers do not address the added computational burden due to an increasing number of hypotheses with the planning horizon, which can grow exponentially. As part of this work, we present a novel algorithm, Hybrid Belief Monte Carlo Planning (HB-MCP) that utilizes the Monte Carlo Tree Search (MCTS) algorithm to solve a POMDP while maintaining a hybrid belief. We illustrate how the upper confidence bound (UCB) exploration bonus can be leveraged to guide the growth of hypotheses trees alongside the belief trees. We then evaluate our approach in highly aliased simulated environments where unresolved data association leads to multi-modal belief hypotheses.