Non-cooperative and cooperative games with a very large number of players have many
applications but remain generally intractable when the number of players increases …

Mean-field games have been used as a theoretical tool to obtain an approximate Nash
equilibrium for symmetric and anonymous $ N $-player games. However, limiting …

Workload autoscaling is widely used in public and private cloud systems to maintain stable
service performance and save resources. However, it remains challenging to set the optimal …

Serverless Function-as-a-Service (FaaS) offers improved programmability for customers, yet
it is not server-" less" and comes at the cost of more complex infrastructure management (eg …

Mean-field reinforcement learning has become a popular theoretical framework for efficiently
approximating large-scale multi-agent reinforcement learning (MARL) problems exhibiting …

Multi-agent reinforcement learning (MARL) has primarily focused on solving a single task in
isolation, while in practice the environment is often evolving, leaving many related tasks to …

We study the sample complexity of reinforcement learning (RL) in Mean-Field Games
(MFGs) with model-based function approximation that requires strategic exploration to find a …

Deep reinforcement learning (DRL) has gained immense success in many applications,
including gaming AI, robotics, and system scheduling. Distributed algorithms and …

Competitive games played by thousands or even millions of players are omnipresent in the
real world, for instance in transportation, communications, or computer networks. However …

Serverless computing, also known as Function-as-a-Service (FaaS), is a significant
development trend in modern software system architecture. The workflow composition of …