Decision Tree Policy

Decision tree policies aim to create interpretable and efficient control strategies for complex systems, addressing the "black box" nature of many machine learning models. Current research focuses on developing algorithms that optimize these trees directly, rather than relying on approximations from other models, employing techniques like policy gradients and mixed-integer linear programming to find optimal or near-optimal tree structures. This work is significant because interpretable policies are crucial for high-stakes applications like healthcare and energy management, enabling better understanding, verification, and trust in automated decision-making systems.

Papers

September 5, 2024

In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Search
Emir Demirović, Christian Schilling, Anna Lukina
Reinforcement Learning Decision Tree Black Box Search Query Tree Specie Neural Network Policy Decision Tree Policy

August 21, 2024

Optimizing Interpretable Decision Tree Policies for Reinforcement Learning
Daniël Vos, Sicco Verwer
Reinforcement Learning Policy Gradient Policy OpTimization Decision Tree Policy

August 14, 2024

Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems
Julian Ruddick, Glenn Ceusters, Gilles Van Kriekinge, Evgenii Genov, Thierry Coosemans, Maarten Messagie
Reinforcement Learning Predictive Control Safe Reinforcement Learning Decision Relevant Information Energy Management Decision Tree Policy Safety Layer Home Energy Management

May 30, 2024

Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
Dimitris Bertsimas, Matthew Peroni
Machine Learning Human Prediction Interpretable Model Interpretable Policy Adaptive Linear Decision Tree Policy

April 3, 2024

Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation
Joo Seung Lee, Malini Mahendra, Anil Aswani
Reinforcement Learning NCD Method Policy Evaluation Decision Tree Policy Mechanical Ventilation

November 21, 2023

An Analysis on the Effects of Evolving the Monte Carlo Tree Search Upper Confidence for Trees Selection Policy on Unimodal, Multimodal and Deceptive Landscapes
Edgar Galvan, Fred Valdez Ameneyro
Multimodal Phenomenon Monte Carlo Tree Search Upper Bound Confidence Bound Selection Policy Optimal Decision Decision Tree Policy Non Convex Landscape

September 23, 2023

Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs
Hector Kohler, Riad Akrour, Philippe Preux
Decision Tree Continuum Limit Actor Critic Algorithm Observable Markov Decision Process Constrained Markov Decision Process Decision Problem Decision Tree Policy

May 14, 2023

A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy Search Over Policy Trees
Marcus Hoerger, Hanna Kurniawati, Dirk Kroese, Nan Ye
Observable Markov Decision Process Automatic Solver Partially Observable Markov Decision Process Decision Tree Policy Continuous Action Semi Markov Decision Process Entropy Search

April 11, 2023

Exact and Cost-Effective Automated Transformation of Neural Network Controllers to Decision Tree Controllers
Kevin Chang, Nathan Dahlin, Rahul Jain, Pierluigi Nuzzo
Neural Network Neural Network Controller Decision Tree Policy Optimal Transformation Soft Decision Tree

January 30, 2023

Optimal Decision Tree Policies for Markov Decision Processes
Daniël Vos, Sicco Verwer
Markov Decision Process Decision Tree Interpretable Policy Decision Tree Policy

December 13, 2022

Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation
Patrick Rehill, Nicholas Biddle
Optimal Policy Multiple Outcome Pareto Frontier Decision Tree Policy Multi Objective Policy Multi Objective Bayesian Optimisation

May 25, 2022

MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning
Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles Kamhoua, Evangelos E. Papalexakis, Fei Fang
Reinforcement Learning Multi Agent Reinforcement Learning Training Agent Decision Tree Policy

April 1, 2022

Building Decision Forest via Deep Reinforcement Learning
Guixuan Wen, Kaigui Wu
Deep Reinforcement Learning Ensemble Learning Deep Reinforcement Ensemble Classifier Decision Tree Policy

March 15, 2022

POETREE: Interpretable Policy Learning with Adaptive Decision Trees
Alizée Pace, Alex J. Chan, Mihaela van der Schaar
Decision Tree Clinical Decision Support System Policy Learning Method Decision Tree Policy

Decision Tree Policy

Papers

In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Search

Optimizing Interpretable Decision Tree Policies for Reinforcement Learning

Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems

Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning

Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation

An Analysis on the Effects of Evolving the Monte Carlo Tree Search Upper Confidence for Trees Selection Policy on Unimodal, Multimodal and Deceptive Landscapes

Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs

A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy Search Over Policy Trees

Exact and Cost-Effective Automated Transformation of Neural Network Controllers to Decision Tree Controllers

Optimal Decision Tree Policies for Markov Decision Processes

Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation

MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning

Building Decision Forest via Deep Reinforcement Learning

POETREE: Interpretable Policy Learning with Adaptive Decision Trees