# Monte Carlo Information-Oriented Planning

arXiv: https://arxiv.org/abs/2103.11345 · HTML: https://mlsanity.com/paper/2103.11345/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [Exploiting Submodular Value Functions For Scaling Up Active Perception](https://mlsanity.com/paper/2009.09696/) (2020)
- [Planning for robotic exploration based on forward simulation](https://mlsanity.com/paper/1502.02474/) (2015)

## Built on by (most cited first, 4 of 4)

- [Smoother Entropy for Active State Trajectory Estimation and Obfuscation in POMDPs](https://mlsanity.com/paper/2108.10227/) (2021)
- [Adaptive Information Belief Space Planning](https://mlsanity.com/paper/2201.05673/) (2022)
- [Previous Knowledge Utilization In Online Anytime Belief Space Planning](https://mlsanity.com/paper/2412.13128/) (2024)
- [Probabilistic Active Goal Recognition](https://mlsanity.com/paper/2507.21846/) (2025)

## Abstract (arXiv)

In this article, we discuss how to solve information-gathering problems expressed as rho-POMDPs, an extension of Partially Observable Markov Decision Processes (POMDPs) whose reward rho depends on the belief state. Point-based approaches used for solving POMDPs have been extended to solving rho-POMDPs as belief MDPs when its reward rho is convex in B or when it is Lipschitz-continuous. In the present paper, we build on the POMCP algorithm to propose a Monte Carlo Tree Search for rho-POMDPs, aiming for an efficient on-line planner which can be used for any rho function. Adaptations are required due to the belief-dependent rewards to (i) propagate more than one state at a time, and (ii) prevent biases in value estimates. An asymptotic convergence proof to epsilon-optimal values is given when rho is continuous. Experiments are conducted to analyze the algorithms at hand and show that they outperform myopic approaches.