243 citations · 267 across the 12 of their papers we have counts for
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cs.LG2023
Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward
Zihan Zhou, Animesh Garg
We propose Structured Exploration with Achievements (SEA), a multi-stage reinforcement learning algorithm designed for achievement-based environments, a particular type of environm…
cs.LG2022★ 2 cited
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization
Zihan Zhou, Wei Fu, Bingliang Zhang +1
We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both loc…
cs.LG2020★ 14 cited
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
Qian Long, Zihan Zhou, Abhibav Gupta +3
In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the…