News
Johns Hopkins APL Trains AI to Adapt Through Video Games
Fri, 11/04/2022 - 16:12
Nicole Choi
The more artificial intelligence agents are deployed in operational scenarios, the more reliably and quickly they will have to navigate unpredictable environments. Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have created Meta Arcade, a suite of arcade games that can be configured and used as training tasks for artificial intelligence systems. Initially developed under the Defense Advanced Research Projects Agency’s& Lifelong Learning Machines program, Meta Arcade trains AI agents to quickly adapt to new and changing scenarios.
Arcade Games for Critical AI Research
The games in Meta Arcade are modeled on classics like Pong and Breakout, common benchmarks in the deep reinforcement learning (DRL) community — experts focused on ways to improve how AI systems train and learn. Unlike a typical game, where settings and features are fixed, a researcher can use Meta Arcade to control the sizes, speeds and colors of game entities, or even create new games. The ease of modifying games through Meta Arcade allows researchers to focus on an algorithm’s specific capability and measure how well an AI agent can handle changes.
The core team behind Meta Arcade includes DRL researcher Ted Staley, AI engineer Chace Ashcraft and researcher Ben Stoler, all from APL’s Research and Exploratory Development Department (REDD). The tool is available to the public through the development platform GitHub, and the team hopes it sparks conversation about other potential tools the DRL community is currently missing. Meta Arcade was also shared at NeurIPS 2021, the Conference and Workshop on Neural Information Processing Systems.
“We needed to develop a tool like Meta Arcade to study and advance our AI research,” said Bart Paulhamus, chief of APL’s Intelligent Systems Center (ISC), which supported the development of Meta Arcade. “By releasing it to the public, APL is accelerating the development of trusted AI for our nation’s most critical challenges. Now, AI researchers can focus their time on AI research, not tool development.”