Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
Recent advances in reinforcement learning in finance
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 …
revolutionized the techniques on data processing and data analysis and brought new …
Online robust reinforcement learning with model uncertainty
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 …
performance over an uncertainty set of MDPs. In this paper, we focus on model-free robust …
Crpo: A new approach for safe reinforcement learning with convergence guarantee
In safe reinforcement learning (SRL) problems, an agent explores the environment to
maximize an expected total reward and meanwhile avoids violation of certain constraints on …
maximize an expected total reward and meanwhile avoids violation of certain constraints on …
A finite-time analysis of two time-scale actor-critic methods
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 …
reinforcement learning algorithms, where the actor uses the policy gradient to improve the …
Improving sample complexity bounds for (natural) actor-critic algorithms
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 …
learning. In the infinite horizon scenario, the finite-sample convergence rate for the AC and …
On finite-time convergence of actor-critic algorithm
Actor-critic algorithm and their extensions have made great achievements in real-world
decision-making problems. In contrast to its empirical success, the theoretical understanding …
decision-making problems. In contrast to its empirical success, the theoretical understanding …
Learning multi-agent behaviors from distributed and streaming demonstrations
This paper considers the problem of inferring the behaviors of multiple interacting experts by
estimating their reward functions and constraints where the distributed demonstrated …
estimating their reward functions and constraints where the distributed demonstrated …
[PDF][PDF] Reinforcement learning for decision-making and control in power systems: Tutorial, review, and vision
With large-scale integration of renewable generation and distributed energy resources
(DERs), modern power systems are confronted with new operational challenges, such as …
(DERs), modern power systems are confronted with new operational challenges, such as …
A general sample complexity analysis of vanilla policy gradient
We adapt recent tools developed for the analysis of Stochastic Gradient Descent (SGD) in
non-convex optimization to obtain convergence and sample complexity guarantees for the …
non-convex optimization to obtain convergence and sample complexity guarantees for the …