The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Robust reinforcement learning (RL) is to find a policy that optimizes the worst-case
performance over an uncertainty set of MDPs. In this paper, we focus on model-free robust …

Actor-critic (AC) methods have exhibited great empirical success compared with other
reinforcement learning algorithms, where the actor uses the policy gradient to improve the …

Modern tasks in reinforcement learning have large state and action spaces. To deal with
them efficiently, one often uses predefined feature map** to represent states and actions …

This paper provides a theoretical understanding of deep Q-Network (DQN) with the
$\varepsilon $-greedy exploration in deep reinforcement learning. Despite the tremendous …

Abstract Temporal-difference learning (TD), coupled with neural networks, is among the
most fundamental building blocks of deep reinforcement learning. However, due to the …

Reinforcement Learning (RL) and its integration with deep learning have achieved
impressive performance in various robotic control tasks, ranging from motion planning and …

The actor-critic (AC) algorithm is a popular method to find an optimal policy in reinforcement
learning. In the infinite horizon scenario, the finite-sample convergence rate for the AC and …

We study the global convergence of policy optimization for finding the Nash equilibria (NE)
in zero-sum linear quadratic (LQ) games. To this end, we first investigate the landscape of …