Optimal Policy

Optimal policy research focuses on finding the best course of action within a given system, often modeled as a Markov Decision Process (MDP), to maximize a desired outcome (e.g., reward, efficiency). Current research emphasizes developing efficient algorithms, such as policy gradient methods and diffusion models, to solve these problems, particularly in complex settings with high dimensionality or uncertainty, often incorporating techniques like variance reduction and bias correction. These advancements are significant for various fields, including robotics, finance, and AI, enabling improved decision-making in scenarios ranging from controlling robots to optimizing resource allocation. The development of more efficient and robust algorithms for finding optimal policies continues to be a central focus.

Papers

January 15, 2024

Push- and Pull-based Effective Communication in Cyber-Physical Systems
Pietro Talli, Federico Mason, Federico Chiariotti, Andrea Zanella
Optimal Policy Cyber Physical System Cyber Physical

December 22, 2023

Federated Q-Learning: Linear Regret Speedup with Low Communication Cost
Zhong Zheng, Fengyu Gao, Lingzhou Xue, Jing Yang
Reinforcement Learning Optimal Policy Single Agent Episodic Markov Decision Process Communication Cost

December 5, 2023

Lights out: training RL agents robust to temporary blindness
N. Ordonez, M. Tromp, P. M. Julbe, W. Böhmer
Loss Function Optimal Policy DQN Agent Hidden Representation RL Agent Blind User

December 1, 2023

A safe exploration approach to constrained Markov decision processes
Tingting Ni, Maryam Kamgarpour
Markov Decision Process Optimal Policy Constrained Markov Decision Process Safe Exploration Policy Parameterization Interior Point

November 30, 2023

Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, Yuta Saito
Optimal Policy Policy Evaluation Portfolio Selection Policy Estimation

November 23, 2023

Fast Policy Learning for Linear Quadratic Control with Entropy Regularization
Xin Guo, Xinyu Li, Renyuan Xu
Policy Gradient Optimal Policy Policy Iteration Entropy Regularization Policy Learning Method

November 22, 2023

Span-Based Optimal Sample Complexity for Average Reward MDPs
Matthew Zurek, Yudong Chen
Sample Complexity Optimal Policy Average Reward Markov Decision Process Average Reward MDPs

November 16, 2023

Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning
Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo
Reinforcement Learning Optimal Policy Reward Shaping Regulatory Compliance

November 15, 2023

On the Foundation of Distributionally Robust Reinforcement Learning
Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou
Optimal Policy Robust Policy Flawed Foundation Robust Reinforcement Learning Distributionally Robust Hybrid Attack

November 14, 2023

Adversarial Imitation Learning On Aggregated Data
Pierre Le Pelletier de Woillemont, Rémi Labory, Vincent Corruble
Reinforcement Learning Optimal Policy Inverse Reinforcement Learning Adversarial Imitation Learning Aggregated Data Linear Reward

November 9, 2023

Anytime-Constrained Reinforcement Learning
Jeremy McMahan, Xiaojin Zhu
Markov Decision Process Optimal Policy Constrained Reinforcement Learning Constrained Markov Decision Process Deterministic Policy Markovian Policy

November 3, 2023

Conditions on Preference Relations that Guarantee the Existence of Optimal Policies
Jonathan Colaço Carr, Prakash Panangaden, Doina Precup
Markov Decision Process Optimal Policy Adverse Condition Approximate Optimization

October 25, 2023

MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning
Dong-Ki Kim, Sungryull Sohn, Lajanugen Logeswaran, Dongsub Shim, Honglak Lee
Reinforcement Learning Multi Agent Reinforcement Learning Optimal Policy Prompt Optimization

October 23, 2023

Safe and Interpretable Estimation of Optimal Treatment Regimes
Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky
Reinforcement Learning Optimal Policy Optimal Treatment Personalized Care

October 17, 2023

Efficient Online Learning with Offline Datasets for Infinite Horizon MDPs: A Bayesian Approach
Dengwang Tang, Rahul Jain, Botao Hao, Zheng Wen
Optimal Policy Online Learning Efficient Reinforcement Learning Bayesian Approach Cumulative Regret Offline Data Infinite Horizon Prior Dependent

October 13, 2023

Optimal Sample Complexity for Average Reward Markov Decision Processes
Shengbo Wang, Jose Blanchet, Peter Glynn
Markov Decision Process Sample Complexity Optimal Policy Policy Learning Average Reward Markov Decision Process Average Reward MDPs

October 11, 2023

Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer
Finn Rietz, Johannes Andreas Stork
Optimal Policy Formality Transfer Diversity Awareness Efficient Adaptation Diverse Behavior Contingency Game

October 9, 2023

When is Agnostic Reinforcement Learning Statistically Tractable?
Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro
Optimal Policy Agnostic Learning PAC Learnability Agnostic PAC Agnostic Reinforcement Learning

October 4, 2023

Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
Sara Klein, Simon Weissmann, Leif Döring
Markov Decision Process Policy Gradient Optimal Policy Convergence Analysis Softmax Policy

October 3, 2023

Probabilistic Reach-Avoid for Bayesian Neural Networks
Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska
Optimal Policy Bayesian Neural Network Model Based Reinforcement Learning Control Synthesis Reach Avoid