KOVA
Kalman Optimization for Value Approximation
**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.
Kalman meets Bellman: Improving Policy Evaluation through Value Tracking
https://paperswithcode.com/paper/kalman-meets-bellman-improving-policy
Kalman meets Bellman: Improving Policy Evaluation through Value Tracking