4 papers · 1 filter
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…
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…