5 papers
PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learning
In-Chang Baek, Sung-Hyun Kim, Sam Earle +4
Reward design plays a pivotal role in the training of game AIs, requiring substantial domain-specific knowledge and human effort. In recent years, several studies have explored rew…
Video Game Level Design as a Multi-Agent Reinforcement Learning Problem
Sam Earle, Zehua Jiang, Eugene Vinitsky +1
Procedural Content Generation via Reinforcement Learning (PCGRL) offers a method for training controllable level designer agents without the need for human datasets, using metrics…
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…
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…
Enhancing Player Enjoyment with a Two-Tier DRL and LLM-Based Agent System for Fighting Games
Shouren Wang, Zehua Jiang, Fernando Sliva +2
Deep reinforcement learning (DRL) has effectively enhanced gameplay experiences and game design across various game genres. However, few studies on fighting game agents have focuse…