83 citations · 127 across the 16 of their papers we have counts for
8 papers · 1 filter
State Trace Rationale As Auxiliary Task in Reinforcement Learning
Muhammad U. Nasir, Alex Vogt, Steven D. James +1
We propose STRAT, an auxiliary task that trains deep reinforcement learning (RL) agents to predict a short textual trace of their own state. Inspired by human spatial navigation, t…
Mortar: Evolving Mechanics for Automatic Game Design
Muhammad U. Nasir, Yuchen Li, Steven James +1
We present Mortar, a system for autonomously evolving game mechanics for automatic game design. Game mechanics define the rules and interactions that govern gameplay, and designing…
PuzzleJAX: A Benchmark for Reasoning and Learning
Sam Earle, Graham Todd, Yuchen Li +5
We introduce PuzzleJAX, a GPU-accelerated puzzle game engine and description language designed to support rapid benchmarking of tree search, reinforcement learning, and LLM reasoni…
GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Games
Yuchen Li, Cong Lin, Muhammad Umair Nasir +3
We introduce GVGAI-LLM, a video game benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). Built on the General Video Game AI fra…
ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search
Sam Earle, Ahmed Khalifa, Muhammad Umair Nasir +4
There is much interest in using large pre-trained models in Automatic Game Design (AGD), whether via the generation of code, assets, or more abstract conceptualization of design id…
Level Generation Through Large Language Models
Graham Todd, Sam Earle, Muhammad Umair Nasir +2
Large Language Models (LLMs) are powerful tools, capable of leveraging their training on natural language to write stories, generate code, and answer questions. But can they genera…
Hierarchically Composing Level Generators for the Creation of Complex Structures
Michael Beukman, Manuel Fokam, Marcel Kruger +5
Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the c…
Augmentative Topology Agents For Open-Ended Learning
Muhammad Umair Nasir, Michael Beukman, Steven James +1
In this work, we tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents and increasingly challenging environments. Unlike previous ope…