Reinforcement Agents for E-Learning Applications

Hamid R. Tizhoosh, Maryam Shokri, Mohamed Kamel

Research output: Chapter in Book/Report/Conference proceedingChapter


Advanced computer systems have become pivotal components for learning. However, we are still faced with many challenges in e-learning environments when developing reliable tools to assist users and facilitate and enhance the learning process. For instance, the problem of creating a user-friendly system that can learn from interaction with dynamic learning requirements and deal with largescale information is still widely unsolved. We need systems that have the ability to communicate and cooperate with the users, learn their preferences and increase the learning efficiency of individual users. Reinforcement learning (RL) is an intelligent technique with the ability to learn from interaction with the environment. It learns from trial and error and generally does not need any training data or a user model. At the beginning of the learning process, the RL agent does not have any knowledge about the actions it should take. After a while, the agent learns which actions yield the maximum reward. The ability of learning from interaction with a dynamic environment and using reward and punishment independent of any training data set makes reinforcement learning a suitable tool for e-learning situations, where subjective user feedback can easily be translated into a reinforcement signal.

Original languageEnglish (US)
Title of host publicationAdvanced Information and Knowledge Processing
Number of pages25
StatePublished - 2007

Publication series

NameAdvanced Information and Knowledge Processing
ISSN (Print)1610-3947
ISSN (Electronic)2197-8441


  • Learning Object
  • Markov Decision Process
  • Multiagent System
  • Partially Observable Markov Decision Process
  • Reinforcement Agent

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence


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