# Multiple Plans are Better than One: Diverse Stochastic Planning

arXiv: https://arxiv.org/abs/2012.15485 · HTML: https://mlsanity.com/paper/2012.15485/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

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

- [Diversity-Aware Policy Optimization for Large Language Model Reasoning](https://mlsanity.com/paper/2505.23433/) (2025)
- [Reinforcement Learning by Guided Safe Exploration](https://mlsanity.com/paper/2307.14316/) (2023)
- [SPEAR: Security Posture Evaluation using AI Planner-Reasoning on Attack-Connectivity Hypergraphs](https://mlsanity.com/paper/2506.01227/) (2025)
- [Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI](https://mlsanity.com/paper/2405.07773/) (2024)
- [Explicit Trajectory Diversity for RL-Based Post-Training of LLM Agents](https://mlsanity.com/paper/2609.38805/) (2026)

## Abstract (arXiv)

In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human's preferences that are either private or complex to model. Consequently, the resulting objective function can only partially capture the specifications and optimizing that may lead to poor performance with respect to the true specifications. Motivated by this challenge, we formulate a problem, called diverse stochastic planning, that aims to generate a set of representative -- small and diverse -- behaviors that are near-optimal with respect to the known objective. In particular, the problem aims to compute a set of diverse and near-optimal policies for systems modeled by a Markov decision process. We cast the problem as a constrained nonlinear optimization for which we propose a solution relying on the Frank-Wolfe method. We then prove that the proposed solution converges to a stationary point and demonstrate its efficacy in several planning problems.