31 citations · 97 across the 9 of their papers we have counts for
6 papers · 1 filter
Guiding Online Reinforcement Learning with Action-Free Offline Pretraining
Deyao Zhu, Yuhui Wang, Jürgen Schmidhuber +1
Offline RL methods have been shown to reduce the need for environment interaction by training agents using offline collected episodes. However, these methods typically require acti…
Mimic: An adaptive algorithm for multivariate time series classification
Yuhui Wang, Diane J. Cook
Time series data are valuable but are often inscrutable. Gaining trust in time series classifiers for finance, healthcare, and other critical applications may rely on creating inte…
Greedy-Step Off-Policy Reinforcement Learning
Yuhui Wang, Qingyuan Wu, Pengcheng He +1
Most of the policy evaluation algorithms are based on the theories of Bellman Expectation and Optimality Equation, which derive two popular approaches - Policy Iteration (PI) and V…
Truly Proximal Policy Optimization
Yuhui Wang, Hao He, Chao Wen +1
Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging task…
Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations
Yuhui Wang, Hao He, Xiaoyang Tan
In real-world scenarios, the observation data for reinforcement learning with continuous control is commonly noisy and part of it may be dynamically missing over time, which violat…
Trust Region-Guided Proximal Policy Optimization
Yuhui Wang, Hao He, Xiaoyang Tan +1
Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging ta…