Inverse Reinforcement Learning based on Critical State
- 10.2991/ifsa-eusflat-15.2015.109How to use a DOI?
- Inverse Reinforcement learning, reward function, reward feature.
Inverse reinforcement learning is tried to search a reward function based on Markov Decision Process. In the IRL topics, experts produce some good traces to make agents learn and adjust the reward function. But the function is difficult to set in some complicate problems. In this paper, Inverse Reinforcement Learning based on Critical State (IRLCS) is proposed to search a succinct and meaningful reward function. IRLCS select a set of reward indexes from whole state space through comparing the difference between the good and bad demonstrations. According to the simulation results, IRLCS can search a good strategy that is similar to experts.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kao-Shing Hwang AU - Tien-Yu Cheng AU - Wei-Cheng Jiang PY - 2015/06 DA - 2015/06 TI - Inverse Reinforcement Learning based on Critical State BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 771 EP - 775 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.109 DO - 10.2991/ifsa-eusflat-15.2015.109 ID - Hwang2015/06 ER -