6 citations · 11 across the 7 of their papers we have counts for
4 papers · 1 filter
medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions
Qianyi Xu, Gousia Habib, Feng Wu +5
Reinforcement Learning (RL) offers a powerful framework for optimizing dynamic treatment regimes (DTRs). However, clinical RL is fundamentally bottlenecked by reward engineering: t…
Effective Multimodal Reinforcement Learning with Modality Alignment and Importance Enhancement
Jinming Ma, Feng Wu, Yingfeng Chen +2
Many real-world applications require an agent to make robust and deliberate decisions with multimodal information (e.g., robots with multi-sensory inputs). However, it is very chal…
Meta Reinforcement Learning with Successor Feature Based Context
Xu Han, Feng Wu
Most reinforcement learning (RL) methods only focus on learning a single task from scratch and are not able to use prior knowledge to learn other tasks more effectively. Context-ba…
Automatic Reward Design via Learning Motivation-Consistent Intrinsic Rewards
Yixiang Wang, Yujing Hu, Feng Wu +1
Reward design is a critical part of the application of reinforcement learning, the performance of which strongly depends on how well the reward signal frames the goal of the design…