Optimal Reinforcement Learning

Optimal reinforcement learning (RL) focuses on developing algorithms that efficiently learn near-optimal policies, minimizing regret and maximizing reward in various environments. Current research emphasizes improving sample efficiency through techniques like offline RL with mixed datasets, meta-learning for transferability across tasks, and the development of algorithms tailored to specific constraints (e.g., adaptivity, safety, continuous actions, or unknown temporal constraints). These advancements are crucial for deploying RL in real-world applications like resource management, robotics, and personalized medicine, where optimal performance and efficient learning are paramount.

Papers

August 13, 2024

Value of Information and Reward Specification in Active Inference and POMDPs
Ran Wei
Reinforcement Learning Full Information Active Inference Net Present Value Free Energy C POMDPs Optimal Reinforcement Learning

February 2, 2024

Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints
Dan Qiao, Yu-Xiang Wang
Multi Agent Reinforcement Learning Markov Game Self Play Optimal Reinforcement Learning Optimal Batch Adaptivity Constraint \Mathcal{o}}$ Revenue Regret

December 23, 2023

Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation
Xin Hao, Changyang She, Phee Lep Yeoh, Yuhong Liu, Branka Vucetic, Yonghui Li
Potential Scalability Gene Level GNN Meta Training Optimal Reinforcement Learning Bandwidth Allocation

November 19, 2023

Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
Kun Yang, Cong Shen, Jing Yang, Shu-ping Yeh, Jerry Sydir
Reinforcement Learning Offline Reinforcement Learning Q Learning Offline Reinforcement Learning Algorithm Optimal Reinforcement Learning Wireless Network Optimization Dataset Mixture

April 30, 2023

Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning
Lunet Yifru, Ali Baheri
Reinforcement Learning Temporal Logic Safe Reinforcement Learning Joint Learning Safety Constraint Constrained Reinforcement Learning Health Policy Recommendation Unknown Constraint Optimal Reinforcement Learning

January 21, 2023

Quasi-optimal Reinforcement Learning with Continuous Actions
Yuhan Li, Wenzhuo Zhou, Ruoqing Zhu
Reinforcement Learning Near Optimal Optimal Learning Optimal Reinforcement Learning Continuous Action Gaussian Policy

December 12, 2022

Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu
Reinforcement Learning Linear MDPs Minimax Optimal Regret Minimax Q Learning Optimal Reinforcement Learning

November 28, 2022

Hypernetworks for Zero-shot Transfer in Reinforcement Learning
Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois Robert Hogan, Gregory Dudek, David Meger
Reinforcement Learning Zero Shot Transfer Attention Hypernetworks DeepMind Control Suite Optimal Reinforcement Learning Meta RL

September 18, 2022

DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs
Khaled Nakhleh, I-Hong Hou
Policy Gradient Restless Multi Armed Bandit Optimal Reinforcement Learning

March 10, 2022

Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, Colin N. Jones
Deep Reinforcement Learning Raw Data Model Based Near Optimal Thermal Control Optimal Reinforcement Learning Optimal Control Input

February 21, 2022

Learning Causal Overhypotheses through Exploration in Children and Computational Models
Eliza Kosoy, Adrian Liu, Jasmine Collins, David M Chan, Jessica B Hamrick, Nan Rosemary Ke, Sandy H Huang, Bryanna Kaufmann, John Canny, Alison Gopnik
Reinforcement Learning Environment Exploration Causal Structure Nine Year Old Child Computational Model Causal Learning Optimal Reinforcement Learning Causal Overhypotheses

January 27, 2022

Generative Adversarial Exploration for Reinforcement Learning
Weijun Hong, Menghui Zhu, Minghuan Liu, Weinan Zhang, Ming Zhou, Yong Yu, Peng Sun
Reinforcement Learning Generative Adversarial Network Optimal Reinforcement Learning Generative Adversarial Exploration

December 10, 2021

How Private Is Your RL Policy? An Inverse RL Based Analysis Framework
Kritika Prakash, Fiza Husain, Praveen Paruchuri, Sujit P. Gujar
Inverse Reinforcement Learning Reinforcement Learning Policy Reward Hacking Optimal Reinforcement Learning