Optimal Reinforcement Learning with Asymmetric Updating in Volatile Environments: a Simulation Study Article Swipe
Mojtaba Rostami Kandroodi
,
Abdol‐Hossein Vahabie
,
Sara Ahmadi
,
Babak Nadjar Araabi
,
Majid Nili Ahmadabadi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.02.15.431283
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.02.15.431283
A bstract The ability to predict the future is essential for decision-making and interaction with the environment to avoid punishment and gain reward. Reinforcement learning algorithms provide a normative way for interactive learning, especially in volatile environments. The optimal strategy for the classic reinforcement learning model is to increase the learning rate as volatility increases. Inspired by optimistic bias in humans, an alternative reinforcement learning model has been developed by adding a punishment learning rate to the classic reinforcement learning model. In this study, we aim to 1) compare the performance of these two models in interaction with different environments, and 2) find optimal parameters for the models. Our simulations indicate that having two different learning rates for rewards and punishments increases performance in a volatile environment. Investigation of the optimal parameters shows that in almost all environments, having a higher reward learning rate compared to the punishment learning rate is beneficial for achieving higher performance which in this case is the accumulation of more rewards. Our results suggest that to achieve high performance, we need a shorter memory window for recent rewards and a longer memory window for punishments. This is consistent with optimistic bias in human behavior.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.02.15.431283
- https://www.biorxiv.org/content/biorxiv/early/2021/02/16/2021.02.15.431283.full.pdf
- OA Status
- green
- Cited By
- 10
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3130948887
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3130948887Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.02.15.431283Digital Object Identifier
- Title
-
Optimal Reinforcement Learning with Asymmetric Updating in Volatile Environments: a Simulation StudyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-16Full publication date if available
- Authors
-
Mojtaba Rostami Kandroodi, Abdol‐Hossein Vahabie, Sara Ahmadi, Babak Nadjar Araabi, Majid Nili AhmadabadiList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.02.15.431283Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2021/02/16/2021.02.15.431283.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/02/16/2021.02.15.431283.full.pdfDirect OA link when available
- Concepts
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Reinforcement learning, Punishment (psychology), Reinforcement, Normative, Computer science, Volatility (finance), Artificial intelligence, Machine learning, Psychology, Social psychology, Econometrics, Mathematics, Epistemology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
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2025: 2, 2024: 6, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2123429050, https://openalex.org/W3034779580, https://openalex.org/W2979918279, https://openalex.org/W2119095023, https://openalex.org/W2013293748, https://openalex.org/W2606776585, https://openalex.org/W2014979870, https://openalex.org/W3162174204, https://openalex.org/W3111213118, https://openalex.org/W2595891121, https://openalex.org/W2052398297, https://openalex.org/W1581202840, https://openalex.org/W2046713808, https://openalex.org/W160989634, https://openalex.org/W2039909349, https://openalex.org/W2070972521, https://openalex.org/W6677916085, https://openalex.org/W2030874315, https://openalex.org/W32403112, https://openalex.org/W2989963295, https://openalex.org/W4226155358, https://openalex.org/W2121863487, https://openalex.org/W2107029055, https://openalex.org/W1557517019, https://openalex.org/W2562554144 |
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