7 citations · 22 across the 10 of their papers we have counts for
4 papers · 1 filter
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…