Learning What to Attend to: Using bisimulation metrics to explore and improve upon what a deep reinforcement learning agent learns
Nele Albers, Miguel Suau de Castro, Frans A. Oliehoek · Research Repository (Delft University of Technology) · 2020
Recent years have seen a surge of algorithms and architectures for deep Re- inforcement Learning (RL), many of which have shown remarkable success for various problems. Yet, little work has attempted to relate the performance of these algorithms and architectures to what the resulting deep RL agents actu- ally learn, and whether this corresponds to what they should ideally learn. Such a comparison may allow for both an improved understanding of why certain algorithms or network architectures perform better than others and the devel- opment of methods that specically address discrepancies between what is and what should be learned.