# Landmark-Assisted Monte Carlo Planning

arXiv: https://arxiv.org/abs/2508.11493 · HTML: https://mlsanity.com/paper/2508.11493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 34 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Successor Options: An Option Discovery Framework for Reinforcement Learning](https://mlsanity.com/paper/1905.05731/) (2019)

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

- [Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary](https://mlsanity.com/paper/2603.06813/) (2026)

## Abstract (arXiv)

Landmarksunicodex2013conditions that must be satisfied at some point in every solution planunicodex2013have contributed to major advancements in classical planning, but they have seldom been used in stochastic domains. We formalize probabilistic landmarks and adapt the UCT algorithm to leverage them as subgoals to decompose MDPs; core to the adaptation is balancing between greedy landmark achievement and final goal achievement. Our results in benchmark domains show that well-chosen landmarks can significantly improve the performance of UCT in online probabilistic planning, while the best balance of greedy versus long-term goal achievement is problem-dependent. The results suggest that landmarks can provide helpful guidance for anytime algorithms solving MDPs.