Deep Reinforcement Learning for General Video Game AI

2018/06/06 by Torrado, Ruben Rodriguez, Bontrager, Philip, Togelius, Julian +2 · 2 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)

paper · doi:10.48550/arxiv.1806.02448

Abstract

The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search. In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.

Cited by

• Spatial Reasoning in LLM Game Agents: Impact of Causal Context and Multi-Step Planning
• PuzzleJAX: A Benchmark for Reasoning and Learning

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