94 citations · 101 across the 5 of their papers we have counts for
5 papers
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…
DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks
Yixiang Wang, Jiqiang Liu, Xiaolin Chang +2
White-box Adversarial Example (AE) attacks towards Deep Neural Networks (DNNs) have a more powerful destructive capacity than black-box AE attacks in the fields of AE strategies. H…
Foster Strengths and Circumvent Weaknesses: a Speech Enhancement Framework with Two-branch Collaborative Learning
Wenxin Tai, Jiajia Li, Yixiang Wang +2
Recent single-channel speech enhancement methods usually convert waveform to the time-frequency domain and use magnitude/complex spectrum as the optimizing target. However, both ma…
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
Yujing Hu, Weixun Wang, Hangtian Jia +5
Reward shaping is an effective technique for incorporating domain knowledge into reinforcement learning (RL). Existing approaches such as potential-based reward shaping normally ma…
Multi-Agent Deep Reinforcement Learning with Adaptive Policies
Yixiang Wang, Feng Wu
We propose a novel approach to address one aspect of the non-stationarity problem in multi-agent reinforcement learning (RL), where the other agents may alter their policies due to…