Summary of Experiments with Encoding Structured Data For Neural Networks, by Sujay Nagesh Koujalgi and Jonathan Dodge
Experiments with Encoding Structured Data for Neural Networks
by Sujay Nagesh Koujalgi, Jonathan Dodge
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AI agent is designed to excel in Battlespace, a sequential domain simulating military wargaming exercises. Combining Monte Carlo Tree Search (MCTS) and Deep Q-Network (DQN) techniques, the agent navigates the environment, avoids obstacles, interacts with adversaries, and captures the flag. To achieve this, researchers explored various encoding techniques to represent complex structured data stored in Python classes, a crucial step towards developing an effective AI agent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to create an AI that can make good decisions in a game called Battlespace. It’s like a big strategy game where the goal is to capture the flag and avoid getting caught. The team used two special techniques: Monte Carlo Tree Search (MCTS) and Deep Q-Network (DQN). These help the agent move around, find its way, and make smart choices. To get started, they had to figure out how to represent complex information in a way that a computer can understand. |
