no code implementations • 17 Apr 2023 • Xiaowen Shi, Ze Wang, Yuanying Cai, Xiaoxu Wu, Fan Yang, Guogang Liao, Yongkang Wang, Xingxing Wang, Dong Wang
There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data.
no code implementations • 3 Mar 2023 • Yuanying Cai, Chuheng Zhang, Wei Shen, Xuyun Zhang, Wenjie Ruan, Longbo Huang
Inspired by the recent success of sequence modeling in RL and the use of masked language model for pre-training, we propose a masked model for pre-training in RL, RePreM (Representation Pre-training with Masked Model), which trains the encoder combined with transformer blocks to predict the masked states or actions in a trajectory.
1 code implementation • 5 Dec 2022 • Yuanying Cai, Chuheng Zhang, Li Zhao, Wei Shen, Xuyun Zhang, Lei Song, Jiang Bian, Tao Qin, TieYan Liu
There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies.
no code implementations • 11 Jun 2020 • Chuheng Zhang, Yuanying Cai, Longbo Huang, Jian Li
In the planning phase, the agent computes a good policy for any reward function based on the dataset without further interacting with the environment.