Search Results for author: Honghao Wei

Found 6 papers, 1 papers with code

Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks

no code implementations25 May 2023 Honghao Wei, Xin Liu, Weina Wang, Lei Ying

This method significantly improves learning by reducing the sample complexity such that the dataset only needs to have sufficient coverage of the stochastic states.


Provably Efficient Model-Free Algorithms for Non-stationary CMDPs

no code implementations10 Mar 2023 Honghao Wei, Arnob Ghosh, Ness Shroff, Lei Ying, Xingyu Zhou

We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a cumulative constraint on the expected utility (cost).

Reinforcement Learning (RL)

Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems

no code implementations13 Dec 2022 Xin Liu, Honghao Wei, Lei Ying

The proposed algorithm is distributed in two aspects: (i) the learned policy is a distributed policy that maps a local state of an agent to its local action and (ii) the learning/training is distributed, during which each agent updates its policy based on its own and neighbors' information.

Multi-agent Reinforcement Learning reinforcement-learning +1

A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processes

no code implementations3 Jun 2021 Honghao Wei, Xin Liu, Lei Ying

This paper presents the first model-free, simulator-free reinforcement learning algorithm for Constrained Markov Decision Processes (CMDPs) with sublinear regret and zero constraint violation.

FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning

2 code implementations4 Oct 2020 Honghao Wei, Lei Ying

In this paper, we propose a new type of Actor, named forward-looking Actor or FORK for short, for Actor-Critic algorithms.

reinforcement-learning Reinforcement Learning (RL)

QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection

no code implementations4 Mar 2019 Honghao Wei, Xiaohan Kang, Weina Wang, Lei Ying

The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation.


Cannot find the paper you are looking for? You can Submit a new open access paper.