Jiancong Huang — accepted AI-conference papers

2021

Hyperparameter Auto-Tuning in Self-Supervised Robotic Learning

Jiancong Huang, Juan Rojas, Matthieu Zimmer, Hongmin Wu, Yisheng Guan, Paul Weng

RA-L 2021

Policy optimization in reinforcement learning requires the selection of numerous hyperparameters across different environments. Fixing them incorrectly may negatively impact optimization performance leading notably to insufficient or redundant learning. Insufficient learning (due to convergence to l

Cited by 10 Source ↗code ↗Scholar ↗

2020

Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning

Yijiong Lin, Jiancong Huang, Matthieu Zimmer, Yisheng Guan, Juan Rojas, Paul Weng

RA-L 2020

Deep Reinforcement Learning (RL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. To alleviate this issue, we propose to exploit the symmetries present in robotic tasks. Intuitively, symmetries from observe

Cited by 51 Source ↗code ↗Scholar ↗