3 papers
cs.LG2020
Policy Learning Using Weak Supervision
Jingkang Wang, Hongyi Guo, Zhaowei Zhu +1
Most existing policy learning solutions require the learning agents to receive high-quality supervision signals such as well-designed rewards in reinforcement learning (RL) or high…
cs.LG2019
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Yang Liu, Hongyi Guo
Learning with noisy labels is a common challenge in supervised learning. Existing approaches often require practitioners to specify noise rates, i.e., a set of parameters controlli…
cs.MA2019
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
Liheng Chen, Hongyi Guo, Yali Du +7
In many real-world problems, a team of agents need to collaborate to maximize the common reward. Although existing works formulate this problem into a centralized learning with dec…