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 …

Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper
presents a comprehensive survey of MARL and its applications. We trace the historical …

We consider a warehouse in which dozens of mobile robots and human pickers work
together to collect and deliver items within the warehouse. The fundamental problem we …

A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL).
However, constructing a standalone RL policy that maps perception to action directly …

Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. Many
approaches have been developed but they can be divided into three main types: centralized …

Deep reinforcement learning (DRL) has found application in numerous use-cases pertaining
to flow control. Multi-agent RL (MARL), a variant of DRL, has shown to be more effective …

Large language models (LLMs) demonstrate their promise in tackling complicated practical
challenges by combining action-based policies with chain of thought (CoT) reasoning …

Open ad hoc teamwork is the problem of training a single agent to efficiently collaborate with
an unknown group of teammates whose composition may change over time. A variable team …

Probabilistic model checking is a technique for formal automated reasoning about software
or hardware systems that operate in the context of uncertainty or stochasticity. It builds upon …

Research on Reinforcement Learning (RL) approaches for discrete optimization problems
has increased considerably, extending RL to an area classically dominated by Operations …