Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline.
Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.
We propose a multiple-komi modification of the AlphaGo Zero/Leela Zero paradigm.
We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.
In this project, we combine AlphaGo algorithm with Curriculum Learning to crack the game of Gomoku.
In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance.
Second, we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields.
There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable.