2 code implementations • 28 Sep 2023 • Mingqi Yuan, Zequn Zhang, Yang Xu, Shihao Luo, Bo Li, Xin Jin, Wenjun Zeng
We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application.
1 code implementation • 26 Jan 2023 • Mingqi Yuan, Bo Li, Xin Jin, Wenjun Zeng
We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-quality intrinsic rewards to enhance exploration in reinforcement learning (RL).
no code implementations • 28 Nov 2022 • Mingqi Yuan, Xin Jin, Bo Li, Wenjun Zeng
We present MEM: Multi-view Exploration Maximization for tackling complex visual control tasks.
no code implementations • 19 Sep 2022 • Mingqi Yuan, Bo Li, Xin Jin, Wenjun Zeng
Exploration is critical for deep reinforcement learning in complex environments with high-dimensional observations and sparse rewards.
1 code implementation • 8 Mar 2022 • 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.
no code implementations • 3 Mar 2022 • Mingqi Yuan
Reinforcement learning (RL) is one of the three basic paradigms of machine learning.
no code implementations • 19 Jul 2021 • Mingqi Yuan, Mon-on Pun, Dong Wang, Yi Chen, Haojun Li
Furthermore, we leverage a variational auto-encoder (VAE) model to capture the life-long novelty of states, which is combined with the global JFI score to form multimodal intrinsic rewards.
no code implementations • 4 Feb 2021 • Mingqi Yuan
Extrapolating beyond-demonstrator (BD) performance through the imitation learning (IL) algorithm aims to learn from and outperform the demonstrator.
no code implementations • 30 Dec 2020 • Mingqi Yuan, Qi Cao, Man-on Pun, Yi Chen
In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness.