The first part of a two-part series of papers provides a survey on recent advances in Deep
Reinforcement Learning (DRL) applications for solving partially observable Markov decision …

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 …

High false-positive rate is a long-standing challenge for anomaly detection algorithms,
especially in high-stake applications. To identify the true anomalies, in practice, analysts or …

Graph data are pervasive in many real-world applications. Recently, increasing attention
has been paid on graph neural networks (GNNs), which aim to model the local graph …

Unlike perfect information games, where all elements are known to every player, imperfect
information games emulate the real-world complexities of decision-making under uncertain …

Games have long been benchmarks and test-beds for AI algorithms. With the development
of AI techniques and the boost of computational power, modern game AI systems have …

As a challenging multi-player card game, DouDizhu has recently drawn much attention for
analyzing competition and collaboration in imperfect-information games. In this paper, we …

Policy distillation, which transfers a teacher policy to a student policy has achieved great
success in challenging tasks of deep reinforcement learning. This teacher-student …

Large Language Models exhibit robust problem-solving capabilities for diverse tasks.
However, most LLM-based agents are designed as specific task solvers with sophisticated …

Model-based deep reinforcement learning has achieved success in various domains that
require high sample efficiencies, such as Go and robotics. However, there are some …