# Synthesising Strategy Improvement and Recursive Algorithms for Solving 2.5 Player Parity Games

arXiv: https://arxiv.org/abs/1607.01474 · HTML: https://mlsanity.com/paper/1607.01474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2016.

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

- [Mungojerrie: Reinforcement Learning of Linear-Time Objectives](https://mlsanity.com/paper/2106.09161/) (2021)

## Abstract (arXiv)

2.5 player parity games combine the challenges posed by 2.5 player reachability games and the qualitative analysis of parity games. These two types of problems are best approached with different types of algorithms: strategy improvement algorithms for 2.5 player reachability games and recursive algorithms for the qualitative analysis of parity games. We present a method that - in contrast to existing techniques - tackles both aspects with the best suited approach and works exclusively on the 2.5 player game itself. The resulting technique is powerful enough to handle games with several million states.