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

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

We introduce DeepNash, an autonomous agent that plays the imperfect information game
Stratego at a human expert level. Stratego is one of the few iconic board games that artificial …

Constructing agents with planning capabilities has long been one of the main challenges in
the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge …

This paper introduces the PettingZoo library and the accompanying Agent Environment
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …

The availability of challenging benchmarks has played a key role in the recent progress of
machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Games are abstractions of the real world, where artificial agents learn to compete and
cooperate with other agents. While significant achievements have been made in various …

Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess
generalization to novel situations as their primary objective (unlike supervised learning …

Games have a long history as benchmarks for progress in artificial intelligence. Approaches
using search and learning produced strong performance across many perfect information …