4 citations · 9 across the 9 of their papers we have counts for
3 papers · 1 filter
From Code to Play: Benchmarking Program Search for Games Using Large Language Models
Manuel Eberhardinger, James Goodman, Alexander Dockhorn +5
Large language models (LLMs) have shown impressive capabilities in generating program code, opening exciting opportunities for applying program synthesis to games. In this work, we…
Strategy Game-Playing with Size-Constrained State Abstraction
Linjie Xu, Diego Perez-Liebana, Alexander Dockhorn
Playing strategy games is a challenging problem for artificial intelligence (AI). One of the major challenges is the large search space due to a diverse set of game components. In…
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
Linjie Xu, Zichuan Liu, Alexander Dockhorn +4
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency. Compared to single agent RL, the sample efficiency for Multi-Agent Reinforcement Learning (MA…