4 papers
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…
Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning
Gaoyuan Liu, Joris de Winter, Yuri Durodie +3
Task and motion planning (TAMP) for robotics manipulation necessitates long-horizon reasoning involving versatile actions and skills. While deterministic actions can be crafted by…
A Task-Efficient Reinforcement Learning Task-Motion Planner for Safe Human-Robot Cooperation
Gaoyuan Liu, Joris de Winter, Kelly Merckaert +3
In a Human-Robot Cooperation (HRC) environment, safety and efficiency are the two core properties to evaluate robot performance. However, safety mechanisms usually hinder task effi…
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…