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

Encyclopedia of Computational Neuroscience

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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Authors and Affiliations

  1. Translational Neuromodeling Unit, Institute of Biomedical Engineering, ETH Zürich and University of Zürich, Wilfriedstrasse 6, Zürich, 8032, Switzerland

    Quentin J. M. Huys

  2. Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zürich, Zürich, Switzerland

    Quentin J. M. Huys

  3. Institute of Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK

    Anthony Cruickshank & Peggy Seriès

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  1. Quentin J. M. Huys
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  2. Anthony Cruickshank
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  3. Peggy Seriès
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Correspondence to Quentin J. M. Huys .

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Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Huys, Q.J.M., Cruickshank, A., Seriès, P. (2014). Reward-Based Learning, Model-Based and Model-Free. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_674-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_674-1

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  1. Latest

    Reward-Based Learning, Model-Based and Model-Free
    Published:
    15 August 2019

    DOI: https://doi.org/10.1007/978-1-4614-7320-6_674-2

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    Reward-Based Learning, Model-Based and Model-Free
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    21 February 2014

    DOI: https://doi.org/10.1007/978-1-4614-7320-6_674-1

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