Encyclopedia of Computational Neuroscience

2015 Edition
| Editors: Dieter Jaeger, Ranu Jung

Reward-Based Learning, Model-Based and Model-Free

  • Quentin J. M. Huys
  • Anthony Cruickshank
  • Peggy Seriès
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6675-8_674

Definition

Reinforcement learning (RL) techniques are a set of solutions for optimal long-term action choice such that actions take into account both immediate and delayed consequences. They fall into two broad classes. Model-based approaches assume an explicit model of the environment and the agent. The model describes the consequences of actions and the associated returns. From this, optimal policies can be inferred. Psychologically, model-based descriptions apply to goal-directed decisions, in which choices reflect current preferences over outcomes. Model-free approaches forgo any explicit knowledge of the dynamics of the environment or the consequences of actions and evaluate how good actions are through trial-and-error learning. Model-free values underlie habitual and Pavlovian conditioned responses that are emitted reflexively when faced with certain stimuli. While model-based techniques have substantial computational demands, model-free techniques require extensive experience.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Quentin J. M. Huys
    • 1
    • 2
  • Anthony Cruickshank
    • 3
  • Peggy Seriès
    • 3
  1. 1.Translational Neuromodeling Unit, Institute of Biomedical EngineeringETH Zürich and University of ZürichZürichSwitzerland
  2. 2.Department of Psychiatry, Psychotherapy and PsychosomaticsHospital of Psychiatry, University of ZürichZürichSwitzerland
  3. 3.Institute of Adaptive and Neural ComputationUniversity of EdinburghEdinburghUK