61 citations · 71 across the 5 of their papers we have counts for
5 papers
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning
Xudong Yu, Chenjia Bai, Hongyi Guo +2
Offline Reinforcement Learning (RL) faces distributional shift and unreliable value estimation, especially for out-of-distribution (OOD) actions. To address this, existing uncertai…
Behavior Contrastive Learning for Unsupervised Skill Discovery
Rushuai Yang, Chenjia Bai, Hongyi Guo +5
In reinforcement learning, unsupervised skill discovery aims to learn diverse skills without extrinsic rewards. Previous methods discover skills by maximizing the mutual informatio…
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…
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…
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…