1 citations · 1 across the 4 of their papers we have counts for
4 papers
Fast, accurate measurement of the worker populations of honey bee colonies using deep learning
Junmin Zhong, Jon F. Harrison, Jennie Si +1
Honey bees play a crucial role in pollination, contributing significantly to global agriculture and ecosystems. Accurately estimating hive populations is essential for understandin…
Actor-Critic Reinforcement Learning with Phased Actor
Ruofan Wu, Junmin Zhong, Jennie Si
Policy gradient methods in actor-critic reinforcement learning (RL) have become perhaps the most promising approaches to solving continuous optimal control problems. However, the t…
Mitigating Estimation Errors by Twin TD-Regularized Actor and Critic for Deep Reinforcement Learning
Junmin Zhong, Ruofan Wu, Jennie Si
We address the issue of estimation bias in deep reinforcement learning (DRL) by introducing solution mechanisms that include a new, twin TD-regularized actor-critic (TDR) method. I…
Long N-step Surrogate Stage Reward to Reduce Variances of Deep Reinforcement Learning in Complex Problems
Junmin Zhong, Ruofan Wu, Jennie Si
High variances in reinforcement learning have shown impeding successful convergence and hurting task performance. As reward signal plays an important role in learning behavior, mul…