4 papers
Hierarchical Support Vector State Partitioning for Distilling Black Box Reinforcement Learning Policies
Senne Deproost, Mehrdad Asadi, Ann Nowé
We introduce State Vector Space Partitioning (SVSP), a novel method to mimic a black box reinforcement learning policy using a set of human-interpretable subpolicies. By partitioni…
Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models
Senne Deproost, Denis Steckelmacher, Ann Nowé
Despite many successful attempts at explaining Deep Reinforcement Learning policies using distillation, it remains difficult to balance the performance-interpretability trade-off a…
Explainable RL Policies by Distilling to Locally-Specialized Linear Policies with Voronoi State Partitioning
Senne Deproost, Dennis Steckelmacher, Ann Nowé
Deep Reinforcement Learning is one of the state-of-the-art methods for producing near-optimal system controllers. However, deep RL algorithms train a deep neural network, that lack…
Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution
Senne Deproost, Denis Steckelmacher, Ann Nowé
With Deep Reinforcement Learning (DRL) being increasingly considered for the control of real-world systems, the lack of transparency of the neural network at the core of RL becomes…