no code implementations • 26 Oct 2021 • Nai-Chieh Huang, Ping-Chun Hsieh, Kuo-Hao Ho, Hsuan-Yu Yao, Kai-Chun Hu, Liang-Chun Ouyang, I-Chen Wu
Policy optimization is a fundamental principle for designing reinforcement learning algorithms, and one example is the proximal policy optimization algorithm with a clipped surrogate objective (PPO-Clip), which has been popularly used in deep reinforcement learning due to its simplicity and effectiveness.
no code implementations • 4 Mar 2021 • Chen-Huan Pi, Kai-Chun Hu, Yu-Ting Huang, Stone Cheng
This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller.
Robotics Systems and Control Systems and Control
no code implementations • NeurIPS Workshop ICBINB 2020 • Kai-Chun Hu, Ping-Chun Hsieh, Ting Han Wei, I-Chen Wu
Deep policy gradient is one of the major frameworks in reinforcement learning, and it has been shown to improve parameterized policies across various tasks and environments.
no code implementations • 24 Apr 2019 • Kai-Chun Hu, Chen-Huan Pi, Ting Han Wei, I-Chen Wu, Stone Cheng, Yi-Wei Dai, Wei-Yuan Ye
In this paper, we point out a fundamental property of the objective in reinforcement learning, with which we can reformulate the policy gradient objective into a perceptron-like loss function, removing the need to distinguish between on and off policy training.