Actor-critic (AC) algorithms have been widely used in decentralized multi-agent systems to
learn the optimal joint control policy. However, existing decentralized AC algorithms either …

We study policy optimization for Markov decision processes (MDPs) with multiple reward
value functions, which are to be jointly optimized according to given criteria such as …

Reinforcement learning (RL) is widely used in applications where one needs to perform
sequential decision-making while interacting with the environment. The standard RL …

Constrained cooperative multi-agent reinforcement learning (MARL) is an emerging
learning framework that has been widely applied to manage multi-agent systems, and many …

Multi-objective reinforcement learning (MORL) is a relatively new field which builds on
conventional Reinforcement Learning (RL) to solve multi-objective problems. One of …

Due to the proliferation of applications and services that run over communication networks,
ranging from video streaming and data analytics to robotics and augmented reality …

We study the usage of information-theoretic measures in learning problems. The first
problem considered is the algorithm-dependent generalization error bound. Conceptually …

To solve large scale machine learning problems, first-order methods such as stochastic
gradient descent and ADAM are the methods of choice because of their low cost per …