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A new Q-learning algorithm based on the Metropolis criterion

Publiceringsår: 2004
Språk: Engelska
Sidor: 2140-2143
Publikation/Tidskrift/Serie: IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics
Volym: 34
Nummer: 5
Dokumenttyp: Artikel i tidskrift
Förlag: IEEE--Institute of Electrical and Electronics Engineers Inc.


The balance between exploration and exploitation is one of the key problems of action selection in Q-learning. Pure exploitation causes the agent to reach the locally optimal policies quickly, whereas excessive exploration degrades the performance of the Q-learning algorithm even if it may accelerate the learning process and allow avoiding the locally optimal policies. In this paper, finding the optimum policy in Q-learning is de scribed as search for the optimum solution in combinatorial optimization. The Metropolis criterion of simulated annealing algorithm is introduced in order to balance exploration and exploitation of Q-learning, and the modified Q-learning algorithm based on this criterion, SA-Q-learning, is presented. Experiments show that SA-Q-learning converges more quickly than Q-learning or Boltzmann exploration, and that the search does not suffer of performance degradation due to excessive exploration.


  • Computer Science
  • reinforcement learning
  • Q-learning
  • metropolis criterion
  • exploitation
  • exploration


  • ISSN: 1083-4419

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