Search Results for author:
Found 11 papers, 2 papers with code
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.
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.
Entropy Regularized Reinforcement Learning with Cascading Networks
Compared to these learning frameworks, one of the major difficulties of RL is the absence of i. i. d.
Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning
We then propose an optimization algorithm that follows the gradient of the composition of the objective and the projection and prove its convergence for linear objectives and arbitrary convex and Lipschitz domain defining inequality constraints.
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts
Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators.
An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions
The Nadaraya-Watson kernel estimator is among the most popular nonparameteric regression technique thanks to its simplicity.
Compatible Natural Gradient Policy Search
Trust-region methods have yielded state-of-the-art results in policy search.
Local Bayesian Optimization of Motor Skills
Bayesian optimization is renowned for its sample efficiency but its application to higher dimensional tasks is impeded by its focus on global optimization.
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement
In order to show the monotonic improvement of our algorithm, we additionally conduct a theoretical analysis of our policy update scheme to derive a lower bound of the change in policy return between successive iterations.
APRIL: Active Preference-learning based Reinforcement Learning
This paper focuses on reinforcement learning (RL) with limited prior knowledge.
