There has been a resurgence of interest in multiagent reinforcement learning (MARL), due
partly to the recent success of deep neural networks. The simplest form of MARL is …

Matrix games like Prisoner's Dilemma have guided research on social dilemmas for
decades. However, they necessarily treat the choice to cooperate or defect as an atomic …

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

Optimization of parameterized policies for reinforcement learning (RL) is an important and
challenging problem in artificial intelligence. Among the most common approaches are …

Fictitious play is a game theoretic iterative procedure meant to learn an equilibrium in
normal form games. However, this algorithm requires that each player has full knowledge of …

We consider a two-player sequential game in which agents have the same reward function
but may disagree on the transition probabilities of an underlying Markovian model of the …

The reinforcement learning problem of complex action control in a multi-player wargame has
been a hot research topic in recent years. In this paper, a game system based on turn-based …

Solving Nash equilibrium is the key challenge in normal-form games with large strategy
spaces, wherein open-ended learning framework provides an efficient approach. Previous …

We solve large two-player zero-sum extensive-form games with perfect recall. We propose a
new algorithm based on fictitious play that significantly reduces memory requirements for …

In this paper we present a preliminary investigation of modelling spatial aspects of security
games within the context of Markov games. Reinforcement learning is a powerful tool for …