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no code implementations • 26 Jan 2023 • Xian Yu, Lei Ying

Risk-sensitive reinforcement learning (RL) has become a popular tool to control the risk of uncertain outcomes and ensure reliable performance in various sequential decision-making problems.

no code implementations • 4 Jan 2023 • Kaiyi Ji, Lei Ying

In this paper, we provide a new solution using a distributed and data-driven bilevel optimization approach, where the lower level is a distributed network utility maximization (NUM) algorithm with concave surrogate utility functions, and the upper level is a data-driven learning algorithm to find the best surrogate utility functions that maximize the sum of true network utility.

no code implementations • 13 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**

no code implementations • 2 Sep 2022 • Zixian Yang, R. Srikant, Lei Ying

Simulation results confirm that the proposed algorithm can stabilize the queues and that it outperforms MaxWeight with empirical mean and MaxWeight with discounted empirical mean.

no code implementations • 27 May 2022 • Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying

Existing studies in the literature cover only some of those implementation choices, and the complexity bounds available are not refined enough to enable rigorous comparison among different implementations.

no code implementations • 26 May 2022 • Zixian Yang, Xin Liu, Lei Ying

To understand the exploration, exploitation, and engagement in these systems, we propose a new model, called MAB-A where "A" stands for abandonment and the abandonment probability depends on the current recommended item and the user's past experience (called state).

no code implementations • 13 Nov 2021 • Jueming Hu, Xuxi Yang, Weichang Wang, Peng Wei, Lei Ying, Yongming Liu

Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic Management (UTM).

no code implementations • 3 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.

no code implementations • NeurIPS 2021 • Xin Liu, Bin Li, Pengyi Shi, Lei Ying

Thus, the overall computational complexity of our algorithm is similar to that of the linear UCB for unconstrained stochastic linear bandits.

no code implementations • 20 Oct 2020 • Xin Liu, Bin Li, Pengyi Shi, Lei Ying

This paper considers constrained online dispatching with unknown arrival, reward and constraint distributions.

2 code implementations • 4 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.

1 code implementation • NeurIPS 2020 • Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant

In this paper, we establish a theoretical comparison between the asymptotic mean-squared error of Double Q-learning and Q-learning.

1 code implementation • NeurIPS 2019 • Harsh Gupta, R. Srikant, Lei Ying

We study two time-scale linear stochastic approximation algorithms, which can be used to model well-known reinforcement learning algorithms such as GTD, GTD2, and TDC.

no code implementations • 4 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.

no code implementations • 3 Feb 2019 • R. Srikant, Lei Ying

We consider the dynamics of a linear stochastic approximation algorithm driven by Markovian noise, and derive finite-time bounds on the moments of the error, i. e., deviation of the output of the algorithm from the equilibrium point of an associated ordinary differential equation (ODE).

no code implementations • 6 Mar 2014 • Kai Zhu, Rui Wu, Lei Ying, R. Srikant

In particular, we consider both the clustering model, where only users (or items) are clustered, and the co-clustering model, where both users and items are clustered, and further, we assume that some users rate many items (information-rich users) and some users rate only a few items (information-sparse users).

no code implementations • 1 Oct 2013 • Jiaming Xu, Rui Wu, Kai Zhu, Bruce Hajek, R. Srikant, Lei Ying

In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure.

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