Meta-inverse Reinforcement Learning Method Based on Relative Entropy

Jisuanji kexue

WU Shao-bo, FU Qi-ming, CHEN Jian-ping, WU Hong-jie, LU You¹ 

[1] 1 School of Electronics and Information Engineering,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China
2 Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency,Suzhou University of Science and Technology,Suzhou, Jiangsu 215009,China
3 Suzhou Key Laboratory of Mobile Network Technology and Application,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China;

关键词: inverse reinforcement learning|meta-learning|reward function|relative entropy|gradient decent;   

DOI  :  10.11896/jsjkx.200700044

来源: DOAJ

【 摘 要 】

Aiming at the problem that traditional inverse reinforcement learning algorithms are slow,imprecise,or even unsolvable when solving the reward function owing to insufficient expert demonstration samples and unknown state transition probabilitie,a meta-reinforcement learning method based on relative entropy is proposed.Using meta-learning methods,the target task learning prior is constructed by integrating a set of meta-training sets that meet the same distribution as the target task.In the model-free reinforcement learning problem,the relative entropy probability model is used to model the reward function and combined with the prior to achieve the goal of quickly solving the reward function of the target task using a small number of samples of the target task.The proposed algorithm and the RE IRL algorithm are applied to the classic Gridworld and Object World pro-blems.Experiments show that the proposed algorithm can still solve the reward function better when the target task lacks a sufficient number of expert demonstration samples and state transition probabilities information

【 授权许可】

Unknown