54 citations · 81 across the 3 of their papers we have counts for
5 papers
A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning
Zhi Wang, Chunlin Chen, Daoyi Dong
While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interfere…
Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction
Yuanyang Zhu, Zhi Wang, Chunlin Chen +1
For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment…
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
Linghan Meng, Yanhui Li, Lin Chen +4
The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models a…
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation
Zhi Wang, Chicheng Zhang, Manish Kumar Singh +2
In many real-world applications, multiple agents seek to learn how to perform highly related yet slightly different tasks in an online bandit learning protocol. We formulate this p…
Lifelong Incremental Reinforcement Learning with Online Bayesian Inference
Zhi Wang, Chunlin Chen, Daoyi Dong
A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes, and to incrementally build upon previous e…