Hamido Fujita教授:Challenges and new directions in Reinforcment learning(强化学习的挑战和新方向)
发布时间: 2021-11-09 浏览次数: 6187

讲座内容:Challenges and new directions in Reinforcment learning(强化学习的挑战和新方向)

讲座人:Hamido Fujita 教授

讲座时间:11月16日 13:00-14:00



   Reinforcement learning (RL) is a branch of machine learning. Compared with the classical supervised learning and unsupervised learning problems of machine learning, the biggest feature of reinforcement learning is learning in interaction. In the interaction with the environment, the agent continuously learns knowledge according to the reward or punishment obtained, so as to adapt to the environment better. The paradigm of RL learning is very similar to the process of human learning knowledge. Therefore, RL is regarded as an important way to realize general AI. Reinforcement learning focuses on finding a balance between exploring the unknown and using current knowledge. In this lecture, I will introduce reinforcement learning and the current challenges and new directions of reinforcement learning, including parameter averaging in distributed deep reinforcement learning, migration between different games through generation model, learning data enhancement, regularization in reinforcement learning, and automatic calculation of Olympiad inequality problems.

Short Bio:

    Dr. Hamido Fujita is professor at Iwate Prefectural University (IPU), Japan, as a director of Intelligent Software Systems. He is the Editor-in-Chief of Knowledge-Based Systems, Elsevier of impact factor (3.325) for 2015. He is vice president of International Society of Applied Intelligence, and also associate Editor-in-chief of Applied Intelligence Journal (Springer). He has given many keynotes in many prestigious international conferences on intelligent system and subjective intelligence. He headed a number of projects including Intelligent HCI, a project related to Mental Cloning as an intelligent user interface between human user and computers and SCOPE project on Virtual Doctor Systems for medical applications.