2024
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
ICLR 2024poster
Learning inherently interpretable policies is a central challenge in the path to developing autonomous agents that humans can trust. Linear policies can justify their decisions while interacting in a dynamic environment, but their reduced expressivity prevents them from solving hard tasks. Instead,…