Control of exploitation-exploration meta-parameter in reinforcement learning

Shin Ishii, Wako Yoshida, Junichiro Yoshimoto

Research output: Contribution to journalArticlepeer-review

155 Citations (Scopus)


In reinforcement learning (RL), the duality between exploitation and exploration has long been an important issue. This paper presents a new method that controls the balance between exploitation and exploration. Our learning scheme is based on model-based RL, in which the Bayes inference with forgetting effect estimates the state-transition probability of the environment. The balance parameter, which corresponds to the randomness in action selection, is controlled based on variation of action results and perception of environmental change. When applied to maze tasks, our method successfully obtains good controls by adapting to environmental changes. Recently, Usher et al. [Science 283 (1999) 549] has suggested that noradrenergic neurons in the locus coeruleus may control the exploitation-exploration balance in a real brain and that the balance may correspond to the level of animal's selective attention. According to this scenario, we also discuss a possible implementation in the brain.

Original languageEnglish
Pages (from-to)665-687
Number of pages23
JournalNeural Networks
Issue number4-6
Publication statusPublished - 2002
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence


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