Kalman meets Bellman: Improving Policy Evaluation through Value Tracking

1 code implementation • 17 Feb 2020 • Shirli Di-Castro Shashua, Shie Mannor

These frameworks can learn uncertainties over the value parameters and exploit them for policy exploration.

Gaussian Processes Reinforcement Learning (RL)

Trust Region Value Optimization using Kalman Filtering

no code implementations • 23 Jan 2019 • Shirli Di-Castro Shashua, Shie Mannor

However, this approach ignores certain distributional properties of both the errors and value parameters.

Deep Robust Kalman Filter

no code implementations • 7 Mar 2017 • Shirli Di-Castro Shashua, Shie Mannor

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.

Decision Making