7 citations · 22 across the 10 of their papers we have counts for
10 papers
RLLTE: Long-Term Evolution Project of Reinforcement Learning
Mingqi Yuan, Zequn Zhang, Yang Xu +4
We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorith…
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning
Mingqi Yuan, Bo Li, Xin Jin +1
We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-quality intrinsic rewards to enhance exploration in reinforcement learning (RL).…
Tackling Visual Control via Multi-View Exploration Maximization
Mingqi Yuan, Xin Jin, Bo Li +1
We present MEM: Multi-view Exploration Maximization for tackling complex visual control tasks. To the best of our knowledge, MEM is the first approach that combines multi-view repr…
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning
Mingqi Yuan, Bo Li, Xin Jin +1
Exploration is critical for deep reinforcement learning in complex environments with high-dimensional observations and sparse rewards. To address this problem, recent approaches pr…
Rényi State Entropy for Exploration Acceleration in Reinforcement Learning
Mingqi Yuan, Man-on Pun, Dong Wang
One of the most critical challenges in deep reinforcement learning is to maintain the long-term exploration capability of the agent. To tackle this problem, it has been recently pr…
Intrinsically-Motivated Reinforcement Learning: A Brief Introduction
Mingqi Yuan
Reinforcement learning (RL) is one of the three basic paradigms of machine learning. It has demonstrated impressive performance in many complex tasks like Go and StarCraft, which i…
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning
Mingqi Yuan, Mon-on Pun, Dong Wang +2
Maintaining the long-term exploration capability of the agent remains one of the critical challenges in deep reinforcement learning. A representative solution is to leverage reward…
Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare
Ming Yuan, Vikas Kumar, Muhammad Aurangzeb Ahmad +1
Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems. While the need for accountability of AI models is near ubiquitous,…
Exploring Beyond-Demonstrator via Meta Learning-Based Reward Extrapolation
Mingqi Yuan, Mao-on Pun
Extrapolating beyond-demonstrator (BD) performance through the imitation learning (IL) algorithm aims to learn from and subsequently outperform the demonstrator. To that end, a rep…
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning
Mingqi Yuan, Qi Cao, Man-on Pun +1
In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness. In contrast to the conventional scheduling algorithms that…