5 citations · 6 across the 10 of their papers we have counts for
8 papers · 1 filter
Plasticine: Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning
Mingqi Yuan, Qi Wang, Guozheng Ma +8
Developing lifelong learning agents is crucial for artificial general intelligence (AGI). However, deep reinforcement learning (RL) systems often suffer from plasticity loss, where…
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning
Mingqi Yuan, Bo Li, Xin Jin +1
Hyperparameter optimization (HPO) is a billion-dollar problem in machine learning, which significantly impacts the training efficiency and model performance. However, achieving eff…
Deep Reinforcement Learning with Hybrid Intrinsic Reward Model
Mingqi Yuan, Bo Li, Xin Jin +1
Intrinsic reward shaping has emerged as a prevalent approach to solving hard-exploration and sparse-rewards environments in reinforcement learning (RL). While single intrinsic rewa…
Adaptive Data Exploitation in Deep Reinforcement Learning
Mingqi Yuan, Bo Li, Xin Jin +1
We introduce ADEPT: Adaptive Data ExPloiTation, a simple yet powerful framework to enhance the **data efficiency** and **generalization** in deep reinforcement learning (RL). Speci…
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning
Mingqi Yuan, Roger Creus Castanyer, Bo Li +3
Extrinsic rewards can effectively guide reinforcement learning (RL) agents in specific tasks. However, extrinsic rewards frequently fall short in complex environments due to the si…
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…