Zhile Yang — accepted AI-conference papers

2023

Fast Counterfactual Inference for History-Based Reinforcement Learning

Haichuan Gao, Tianren Zhang, Zhile Yang, Yuqing Guo, Jinsheng Ren, Shangqi Guo +1

AAAI 2023technical

Incorporating sequence-to-sequence models into history-based Reinforcement Learning (RL) provides a general way to extend RL to partially-observable tasks. This method compresses history spaces according to the correlations between historical observations and the rewards. However, they do not adjust…

Cited by 3 Source ↗PDF ↗Scholar ↗

2020

Adaptability Preserving Domain Decomposition for Stabilizing Sim2Real Reinforcement Learning

Haichuan Gao, Zhile Yang, Xin Su, Tian Tan, Feng Chen

IROS 2020poster

In sim-to-real transfer of Reinforcement Learning (RL) policies for robot tasks, Domain Randomization (DR) is a widely used technique for improving adaptability. However, in DR there is a conflict between adaptability and training stability, and heavy DR tends to result in instability or even failur…

Cited by 6 Source ↗Scholar ↗