Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Robot navigation in crowded public spaces is a complex task that requires addressing a
variety of engineering and human factors challenges. These challenges have motivated a …

A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Agent-based modeling (ABM) is a novel computational methodology for representing the
behavior of individuals in order to study social phenomena. Its use is rapidly growing in …

In recent years there have been great strides in artificial intelligence (AI), with games often
serving as challenge problems, benchmarks, and milestones for progress. Poker has served …

OpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player (single …

Abstract We show that Optimistic Hedge--a common variant of multiplicative-weights-
updates with recency bias--attains ${\rm poly}(\log T) $ regret in multi-player general-sum …

Despite its important applications in Machine Learning, min-max optimization of objective
functions that are nonconvex-nonconcave remains elusive. Not only are there no known first …

The cornerstone underpinning deep learning is the guarantee that gradient descent on an
objective converges to local minima. Unfortunately, this guarantee fails in settings, such as …