Policy Evaluation
Kalman Optimization for Value Approximation
Introduced by Shashua et al. in Kalman meets Bellman: Improving Policy Evaluation through Value TrackingKalman 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.
Source: Kalman meets Bellman: Improving Policy Evaluation through Value Tracking
Papers
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Components
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