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      Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration

      1 , 2 , 1 , 1
      Philosophical Transactions of the Royal Society B: Biological Sciences
      The Royal Society

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          Abstract

          Many large and small decisions we make in our daily lives-which ice cream to choose, what research projects to pursue, which partner to marry-require an exploration of alternatives before committing to and exploiting the benefits of a particular choice. Furthermore, many decisions require re-evaluation, and further exploration of alternatives, in the face of changing needs or circumstances. That is, often our decisions depend on a higher level choice: whether to exploit well known but possibly suboptimal alternatives or to explore risky but potentially more profitable ones. How adaptive agents choose between exploitation and exploration remains an important and open question that has received relatively limited attention in the behavioural and brain sciences. The choice could depend on a number of factors, including the familiarity of the environment, how quickly the environment is likely to change and the relative value of exploiting known sources of reward versus the cost of reducing uncertainty through exploration. There is no known generally optimal solution to the exploration versus exploitation problem, and a solution to the general case may indeed not be possible. However, there have been formal analyses of the optimal policy under constrained circumstances. There have also been specific suggestions of how humans and animals may respond to this problem under particular experimental conditions as well as proposals about the brain mechanisms involved. Here, we provide a brief review of this work, discuss how exploration and exploitation may be mediated in the brain and highlight some promising future directions for research.

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          Most cited references23

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          Separate neural systems value immediate and delayed monetary rewards.

          When humans are offered the choice between rewards available at different points in time, the relative values of the options are discounted according to their expected delays until delivery. Using functional magnetic resonance imaging, we examined the neural correlates of time discounting while subjects made a series of choices between monetary reward options that varied by delay to delivery. We demonstrate that two separate systems are involved in such decisions. Parts of the limbic system associated with the midbrain dopamine system, including paralimbic cortex, are preferentially activated by decisions involving immediately available rewards. In contrast, regions of the lateral prefrontal cortex and posterior parietal cortex are engaged uniformly by intertemporal choices irrespective of delay. Furthermore, the relative engagement of the two systems is directly associated with subjects' choices, with greater relative fronto-parietal activity when subjects choose longer term options.
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            Anterior cingulate conflict monitoring and adjustments in control.

            Conflict monitoring by the anterior cingulate cortex (ACC) has been posited to signal a need for greater cognitive control, producing neural and behavioral adjustments. However, the very occurrence of behavioral adjustments after conflict has been questioned, along with suggestions that there is no direct evidence of ACC conflict-related activity predicting subsequent neural or behavioral adjustments in control. Using the Stroop color-naming task and controlling for repetition effects, we demonstrate that ACC conflict-related activity predicts both greater prefrontal cortex activity and adjustments in behavior, supporting a role of ACC conflict monitoring in the engagement of cognitive control.
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              Reinforcement Learning: A Survey

              This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.
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                Author and article information

                Journal
                Philosophical Transactions of the Royal Society B: Biological Sciences
                Phil. Trans. R. Soc. B
                The Royal Society
                0962-8436
                1471-2970
                March 30 2007
                May 29 2007
                March 29 2007
                May 29 2007
                : 362
                : 1481
                : 933-942
                Affiliations
                [1 ]Department of Psychology and Center for the Study of Brain, Mind and Behaviour, Princeton UniversityPrinceton, NJ 08540, USA
                [2 ]Department of Psychiatry, University of PittsburghPittsburgh, PA 15213, USA
                Article
                10.1098/rstb.2007.2098
                2430007
                17395573
                c78f7f79-1205-43ca-a821-3e5a15305e35
                © 2007
                History

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