# Kalman Temporal Differences

arXiv: https://arxiv.org/abs/1406.3270 · HTML: https://mlsanity.com/paper/1406.3270/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2014.

## Built on by (most cited first, 10 of 10)

- [Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions](https://mlsanity.com/paper/2411.00465/) (2024)
- [Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness](https://mlsanity.com/paper/2105.09992/) (2021)
- [Toward Efficient Gradient-Based Value Estimation](https://mlsanity.com/paper/2301.13757/) (2023)
- [Fast Value Tracking for Deep Reinforcement Learning](https://mlsanity.com/paper/2403.13178/) (2024)
- [Lifelong Reinforcement Learning via Neuromodulation](https://mlsanity.com/paper/2408.08446/) (2024)
- [Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation](https://mlsanity.com/paper/2112.15156/) (2021)
- [AKF-SR: Adaptive Kalman Filtering-based Successor Representation](https://mlsanity.com/paper/2204.00049/) (2022)
- [Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users](https://mlsanity.com/paper/2110.13213/) (2021)
- [Bayesian learning for the stochastic shortest path problem](https://mlsanity.com/paper/2606.04845/) (2026)
- [Generalized Kalman filter based temporal difference reinforcement learning](https://mlsanity.com/paper/2607.20010/) (2026)

## Abstract (arXiv)

Because reinforcement learning suffers from a lack of scalability, online value (and Q-) function approximation has received increasing interest this last decade. This contribution introduces a novel approximation scheme, namely the Kalman Temporal Differences (KTD) framework, that exhibits the following features: sample-efficiency, non-linear approximation, non-stationarity handling and uncertainty management. A first KTD-based algorithm is provided for deterministic Markov Decision Processes (MDP) which produces biased estimates in the case of stochastic transitions. Than the eXtended KTD framework (XKTD), solving stochastic MDP, is described. Convergence is analyzed for special cases for both deterministic and stochastic transitions. Related algorithms are experimented on classical benchmarks. They compare favorably to the state of the art while exhibiting the announced features.