In the paper, we propose a class of accelerated zeroth-order and first-order momentum
methods for both nonconvex mini-optimization and minimax-optimization. Specifically, we …

In reinforcement learning (RL), the goal is to obtain an optimal policy, for which the optimality
criterion is fundamentally important. Two major optimality criteria are average and …

We study the problem of computing deterministic optimal policies for constrained Markov
decision processes (MDPs) with continuous state and action spaces, which are widely …

Bi-level optimization (BO) has become a fundamental mathematical framework for
addressing hierarchical machine learning problems. As deep learning models continue to …

Policy gradient (PG) methods are successful approaches to deal with continuous
reinforcement learning (RL) problems. They learn stochastic parametric (hyper) policies by …

Existing distributed cooperative multi-agent reinforcement learning (MARL) frameworks
usually assume undirected coordination graphs and communication graphs, while …

Wireless systems resource allocation refers to perpetual and challenging nonconvex
constrained optimization tasks, which are especially timely in modern communications and …

Unmanned Vehicles (UVs) functioning as autonomous agents are anticipated to play a
crucial role in the 6th Generation of wireless networks. Their seamless integration, cost …

Electronically tunable metasurfaces, or Intelligent Reflecting Surfaces (IRSs), are a popular
technology for achieving high spectral efficiency in modern wireless systems by shaping …

Achieving distributed reinforcement learning (RL) for large-scale cooperative multi-agent
systems (MASs) is challenging because:(i) each agent has access to only limited …