Methods › Reinforcement Learning › Policy Evaluation › KOVA
Kalman Optimization for Value Approximation
KOVA
Introduced by Shirli Di-Castro Shashua et al. in Kalman meets Bellman: Improving Policy Evaluation through Value Tracking
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Kalman Optimization for Value Approximation, or KOVA is a general framework for addressing uncertainties while approximating value-based functions in deep RL domains. KOVA minimizes a regularized objective function that concerns both parameter and noisy return uncertainties. It is feasible when using non-linear approximation functions as DNNs and can estimate the value in both on-policy and off-policy settings. It can be incorporated as a policy evaluation component in policy optimization algorithms.
Papers archive 2025-07-28
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Kalman meets Bellman: Improving Policy Evaluation through Value Tracking 17 Feb 2020 · 1 repository · arXiv:2002.07171
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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