Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

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 …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Automated algorithm configuration relieves users from tedious, trial-and-error tuning tasks. A
popular algorithm configuration tuning paradigm is dynamic algorithm configuration (DAC) …

Abstract The field of Multi-Agent Reinforcement Learning (MARL) is currently facing a
reproducibility crisis. While solutions for standardized reporting have been proposed to …

Benchmarks play an important role in the development of machine learning algorithms. For
example, research in reinforcement learning (RL) has been heavily influenced by available …

We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning
(MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a …

Evaluating deep multiagent reinforcement learning (MARL) algorithms is complicated by
stochasticity in training and sensitivity of agent performance to the behavior of other agents …

Cooperative multi-agent reinforcement learning (MARL), where agents coordinates with
teammate (s) for a shared goal, may sustain non-stationary caused by the policy change of …

Human-AI coordination aims to develop AI agents capable of effectively coordinating with
human partners, making it a crucial aspect of cooperative multi-agent reinforcement learning …