Search Results for author:
Found 3 papers, 2 papers with code
A Distributional Analogue to the Successor Representation
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process.
Distributional Reinforcement Learning
Model-based Reinforcement Learning
+1
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
To conclude, we develop a distribution-aware procedure which finds such paths, navigating away from noisy neighborhoods in order to improve the robustness of a policy.
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
We demonstrate the effectiveness of such an algorithm in a synthetic control problem.
