# Partially Observable Stochastic Games with Neural Perception Mechanisms

arXiv: https://arxiv.org/abs/2310.11566 · HTML: https://mlsanity.com/paper/2310.11566/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker](https://mlsanity.com/paper/1701.01724/) (2017)
- [Combining Deep Reinforcement Learning and Search for Imperfect-Information Games](https://mlsanity.com/paper/2007.13544/) (2020)
- [Rethinking Formal Models of Partially Observable Multiagent Decision Making](https://mlsanity.com/paper/1906.11110/) (2019)
- [Solving Zero-Sum One-Sided Partially Observable Stochastic Games](https://mlsanity.com/paper/2010.11243/) (2020)
- [HSVI can solve zero-sum Partially Observable Stochastic Games](https://mlsanity.com/paper/2210.14640/) (2022)
- [Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information](https://mlsanity.com/paper/1606.06888/) (2016)
- [The Stackelberg Equilibrium for One-sided Zero-sum Partially Observable Stochastic Games](https://mlsanity.com/paper/2109.08339/) (2021)
- [Optimal Continuous State POMDP Planning with Semantic Observations: A Variational Approach](https://mlsanity.com/paper/1807.08229/) (2018)
- [Strategy Synthesis for Zero-Sum Neuro-Symbolic Concurrent Stochastic Games](https://mlsanity.com/paper/2202.06255/) (2022)
- [Finite-horizon Equilibria for Neuro-symbolic Concurrent Stochastic Games](https://mlsanity.com/paper/2205.07546/) (2022)
- [Point-Based Value Iteration for POMDPs with Neural Perception Mechanisms](https://mlsanity.com/paper/2306.17639/) (2023)
- [Zero-Sum Stochastic Games with Partial Information and Average Payoff](https://mlsanity.com/paper/1409.4030/) (2014)
- [Partially Observable Discrete-time Discounted Markov Games with General Utility](https://mlsanity.com/paper/2211.07888/) (2022)

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

- [Collision Avoidance Verification of Multiagent Systems with Learned Policies](https://mlsanity.com/paper/2403.03314/) (2024)
- [HSVI-based Online Minimax Strategies for Partially Observable Stochastic Games with Neural Perception Mechanisms](https://mlsanity.com/paper/2404.10679/) (2024)

## Abstract (arXiv)

Stochastic games are a well established model for multi-agent sequential decision making under uncertainty. In practical applications, though, agents often have only partial observability of their environment. Furthermore, agents increasingly perceive their environment using data-driven approaches such as neural networks trained on continuous data. We propose the model of neuro-symbolic partially-observable stochastic games (NS-POSGs), a variant of continuous-space concurrent stochastic games that explicitly incorporates neural perception mechanisms. We focus on a one-sided setting with a partially-informed agent using discrete, data-driven observations and another, fully-informed agent. We present a new method, called one-sided NS-HSVI, for approximate solution of one-sided NS-POSGs, which exploits the piecewise constant structure of the model. Using neural network pre-image analysis to construct finite polyhedral representations and particle-based representations for beliefs, we implement our approach and illustrate its practical applicability to the analysis of pedestrian-vehicle and pursuit-evasion scenarios.