# Experiments with Encoding Structured Data for Neural Networks

arXiv: https://arxiv.org/abs/2402.10290 · HTML: https://mlsanity.com/paper/2402.10290/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 15 Feb 2024.

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

- [Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning](https://mlsanity.com/paper/2206.15378/) (2022)
- [Explaining Reinforcement Learning to Mere Mortals: An Empirical Study](https://mlsanity.com/paper/1903.09708/) (2019)

## Abstract (arXiv)

The project's aim is to create an AI agent capable of selecting good actions in a game-playing domain called Battlespace. Sequential domains like Battlespace are important testbeds for planning problems, as such, the Department of Defense uses such domains for wargaming exercises. The agents we developed combine Monte Carlo Tree Search (MCTS) and Deep Q-Network (DQN) techniques in an effort to navigate the game environment, avoid obstacles, interact with adversaries, and capture the flag. This paper will focus on the encoding techniques we explored to present complex structured data stored in a Python class, a necessary precursor to an agent.