Generalized Kalman filter based temporal difference reinforcement learning
In this paper, we present a generalized temporal-difference (TD) reinforcement learning framework based on the theory of conditional expectations. The value and action-value (Q-value) functions are treated as uncertain quantities, and their estimation is formulated as a stochastic inference problem. Unlike classical Kalman-based temporal-difference learning, which relies on linear-Gaussian assumptions, the proposed formulation is derived directly from the conditional expectation framework and naturally extends to nonlinear models and non-Gaussian probability distributions. The proposed method recursively estimates not only the conditional expectation of the value function but also its sec...
| Authors | Vasos Arnaoutis, Eric Lutters, Bojana Rosić |
|---|---|
| Primary category | cs.LG |
| Published | 22 July 2026 |
| Last updated at source | 23 July 2026 |
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