Oppositional extension of reinforcement learning techniques

M. Mahootchi, H. R. Tizhoosh, K. Ponnambalam

Research output: Contribution to journalArticlepeer-review


In this paper, we present different opposition schemes for four reinforcement learning methods: Q-learning, Q(λ), Sarsa, and Sarsa(λ) under assumptions that are reasonable for many real-world problems where type-II opposites generally better reflect the nature of the problem at hand. It appears that the aggregation of opposition-based schemes with regular learning methods can significantly speed up the learning process, especially where the number of observations is small or the state space is large. We verify the performance of the proposed methods using two different applications: a grid-world problem and a single water reservoir management problem.

Original languageEnglish (US)
Pages (from-to)101-114
Number of pages14
JournalInformation Sciences
StatePublished - Aug 10 2014


  • Grid world
  • Opposition-based learning
  • Q-learning
  • Reinforcement learning
  • Reservoir management
  • Sarsa

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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