Griddly: A Platform For AI Research In Games

Abstract

In recent years, there have been immense breakthroughs in Game AI research, particularly with Reinforcement Learning (RL). Despite their success, the underlying games are usually implemented with their own preset environments and game mechanics, thus making it difficult for researchers to prototype different game environments. However, testing the RL agents against a variety of game environments is critical for recent effort to study generalization in RL and avoid the problem of overfitting that may otherwise occur. In this paper, we present Griddly as a new platform for Game AI research that provides a unique combination of highly configurable games, different observer types and an efficient C++ core engine. Additionally, we present a series of baseline experiments to study the effect of different observation configurations and generalization ability of RL agents.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keybamford2020griddly

Related papers

• Pytag: Challenges And Opportunities For Reinforcement Learning In Tabletop Games (2023)4.52
• Eden: A Unified Environment Framework For Booming Reinforcement Learning Algorithms (2021)0.00
• The AI Arena: A Framework For Distributed Multi-agent Reinforcement Learning (2021)0.00
• Aixijs: A Software Demo For General Reinforcement Learning (2017)0.00
• Deep RTS: A Game Environment For Deep Reinforcement Learning In Real-time Strategy Games (2018)16.97
• A Survey Of Deep Reinforcement Learning In Video Games (2019)0.00
• Jumanji: A Diverse Suite Of Scalable Reinforcement Learning Environments In JAX (2023)0.00
• Google Research Football: A Novel Reinforcement Learning Environment (2019)17.22