Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

In the last few years, reinforcement learning (RL), also called adaptive (or approximate)
dynamic programming, has emerged as a powerful tool for solving complex sequential …

Abstract We propose Adversarially Trained Actor Critic (ATAC), a new model-free algorithm
for offline reinforcement learning (RL) under insufficient data coverage, based on the …

This paper analyzes a two-timescale stochastic algorithm framework for bilevel optimization.
Bilevel optimization is a class of problems which exhibits a two-level structure, and its goal is …

This paper investigates the electric vehicle (EV) charging scheduling problem for public EV
charging stations (EVCSs) that can accommodate heterogeneous charging demands …

Abstract Generative Adversarial Networks (GANs) excel at creating realistic images with
complex models for which maximum likelihood is infeasible. However, the convergence of …

When function approximation is used, solving the Bellman optimality equation with stability
guarantees has remained a major open problem in reinforcement learning for decades. The …

We propose RUDDER, a novel reinforcement learning approach for delayed rewards in
finite Markov decision processes (MDPs). In MDPs the Q-values are equal to the expected …

We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across
distributed sources of non-independent-and-identically-distributed data sources subject to …

Most existing deep reinforcement learning (DRL) frameworks consider either discrete action
space or continuous action space solely. Motivated by applications in computer games, we …