Search Results for author: Mingqi Yuan

Found 8 papers, 1 papers with code

Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning

no code implementations26 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).

reinforcement-learning reinforcement Learning

Tackling Visual Control via Multi-View Exploration Maximization

no code implementations28 Nov 2022 Mingqi Yuan, Xin Jin, Bo Li, Wenjun Zeng

We present MEM: Multi-view Exploration Maximization for tackling complex visual control tasks.

Representation Learning

Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning

no code implementations19 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.

Atari Games reinforcement-learning +2

Rényi State Entropy for Exploration Acceleration in Reinforcement Learning

1 code implementation8 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.

reinforcement-learning reinforcement Learning

Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning

no code implementations19 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.

Efficient Exploration Fairness +2

Hybrid Adversarial Imitation Learning

no code implementations4 Feb 2021 Mingqi Yuan

Extrapolating beyond-demonstrator (BD) performance through the imitation learning (IL) algorithm aims to learn from and outperform the demonstrator.

Imitation Learning

Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning

no code implementations30 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.

Distributed Optimization Fairness +3

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