# Deep Robust Kalman Filter

arXiv: https://arxiv.org/abs/1703.02310 · HTML: https://mlsanity.com/paper/1703.02310/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch](https://mlsanity.com/paper/2007.01174/) (2020)
- [On the Stochastic Stability of Deep Markov Models](https://mlsanity.com/paper/2111.04601/) (2021)
- [Twice Regularized Markov Decision Processes: The Equivalence between Robustness and Regularization](https://mlsanity.com/paper/2303.06654/) (2023)

## Abstract (arXiv)

A Robust Markov Decision Process (RMDP) is a sequential decision making model that accounts for uncertainty in the parameters of dynamic systems. This uncertainty introduces difficulties in learning an optimal policy, especially for environments with large state spaces. We propose two algorithms, RTD-DQN and Deep-RoK, for solving large-scale RMDPs using nonlinear approximation schemes such as deep neural networks. The RTD-DQN algorithm incorporates the robust Bellman temporal difference error into a robust loss function, yielding robust policies for the agent. The Deep-RoK algorithm is a robust Bayesian method, based on the Extended Kalman Filter (EKF), that accounts for both the uncertainty in the weights of the approximated value function and the uncertainty in the transition probabilities, improving the robustness of the agent. We provide theoretical results for our approach and test the proposed algorithms on a continuous state domain.