Balanced Q-learning: Combining the influence of optimistic and pessimistic targets Article Swipe
Thommen George Karimpanal
,
Hung Lê
,
Majid Abdolshah
,
Santu Rana
,
Sunil Gupta
,
Truyen Tran
,
Svetha Venkatesh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.artint.2023.104021
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.artint.2023.104021
The optimistic nature of the Q−learning target leads to an overestimation bias, which is an inherent problem associated with standard Q−learning. Such a bias fails to account for the possibility of low returns, particularly in risky scenarios. However, the existence of biases, whether overestimation or underestimation, need not necessarily be undesirable. In this paper, we analytically examine the utility of biased learning, and show that specific types of biases may be preferable, depending on the scenario. Based on this finding, we design a novel reinforcement learning algorithm, Balanced Q-learning, in which the target is modified to be a convex combination of a pessimistic and an optimistic term, whose associated weights are determined online, analytically. Such a balanced target inherently promotes risk-averse behavior, which we examine through the lens of the agent's exploration. We prove the convergence of this algorithm in a tabular setting, and empirically demonstrate its consistently good learning performance in various environments.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.artint.2023.104021
- OA Status
- hybrid
- Cited By
- 5
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3211287969
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3211287969Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.artint.2023.104021Digital Object Identifier
- Title
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Balanced Q-learning: Combining the influence of optimistic and pessimistic targetsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-09-28Full publication date if available
- Authors
-
Thommen George Karimpanal, Hung Lê, Majid Abdolshah, Santu Rana, Sunil Gupta, Truyen Tran, Svetha VenkateshList of authors in order
- Landing page
-
https://doi.org/10.1016/j.artint.2023.104021Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.artint.2023.104021Direct OA link when available
- Concepts
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Pessimism, Computer science, Convergence (economics), Reinforcement learning, Term (time), Artificial intelligence, Q-learning, Econometrics, Machine learning, Mathematics, Economics, Quantum mechanics, Economic growth, Physics, Epistemology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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