Search Results for author:
Found 7 papers, 1 papers with code
Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning
We empirically demonstrate that INTERPRETER compact tree programs match oracles across a diverse set of sequential decision tasks and evaluate the impact of our design choices on interpretability and performances.
PID Tuning using Cross-Entropy Deep Learning: a Lyapunov Stability Analysis
We perform this stability analysis on a LB adaptive control system whose adaptive parameters are determined using a Cross-Entropy Deep Learning method.
Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop
What reinforcement learning paradigms, are the most suited to develop interpretable agents?
Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs
We show in this paper that deep RL can fail even on simple toy tasks of this class.
Interpretable Decision Tree Search as a Markov Decision Process
Finding an optimal decision tree for a supervised learning task is a challenging combinatorial problem to solve at scale.
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
When comparing several RL algorithms, a major question is how many executions must be made and how can we ensure that the results of such a comparison are theoretically sound.
Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning
A given supervised classification task is modeled as a Markov decision problem (MDP) and then augmented with additional actions that gather information about the features, equivalent to building a DT.
