1 code implementation • 15 Oct 2022 • Tianying Ji, Yu Luo, Fuchun Sun, Mingxuan Jing, Fengxiang He, Wenbing Huang
Our follow-up derived bounds reveal the relationship between model shifts and performance improvement.
Model-based Reinforcement Learning reinforcement-learning +1
1 code implementation • 10 Jun 2021 • Mingxuan Jing, Wenbing Huang, Fuchun Sun, Xiaojian Ma, Tao Kong, Chuang Gan, Lei LI
In particular, we propose an Expectation-Maximization(EM)-style algorithm: an E-step that samples the options of expert conditioned on the current learned policy, and an M-step that updates the low- and high-level policies of agent simultaneously to minimize the newly proposed option-occupancy measurement between the expert and the agent.
no code implementations • 16 Nov 2019 • Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Chao Yang, Bin Fang, Huaping Liu
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations.
no code implementations • 18 May 2018 • Mingxuan Jing, Xiaojian Ma, Fuchun Sun, Huaping Liu
Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks.
no code implementations • 12 May 2018 • Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu
The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task.