Exploration Policy

Exploration policy in reinforcement learning focuses on designing efficient strategies for agents to discover rewarding states or actions within complex environments, crucial for optimal decision-making. Current research emphasizes improving exploration efficiency in various contexts, including multi-agent systems, long-horizon tasks, and sparse-reward scenarios, often employing techniques like hierarchical reinforcement learning, Monte Carlo tree search, and meta-learning to optimize exploration-exploitation trade-offs. These advancements are significant for improving the sample efficiency and robustness of reinforcement learning algorithms across diverse applications, such as robotics, recommender systems, and autonomous navigation.

Papers

October 31, 2024

Deterministic Exploration via Stationary Bellman Error Maximization
Sebastian Griesbach, Carlo D'Eramo
Reinforcement Learning Environment Exploration Intrinsic Reward Exploration Policy Bellman Error Random Exploration

August 3, 2024

Walk Wisely on Graph: Knowledge Graph Reasoning with Dual Agents via Efficient Guidance-Exploration
Zijian Wang, Bin Wang, Haifeng Jing, Huayu Li, Hongbo Dou
Graph Drawing Hierarchical Reinforcement Learning Multi Hop Reasoning Reasoning Path Malicious Agent Knowledge Graph Reasoning Exploration Policy

July 7, 2024

Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
Liam Schramm, Abdeslam Boularias
Monte Carlo Tree Search Sampling Based Motion Planning Exploration Policy State Occupancy Matching

May 21, 2024

A Novel Methodology for Autonomous Planetary Exploration Using Multi-Robot Teams
Sarah Swinton, Jan-Hendrik Ewers, Euan McGookin, David Anderson, Douglas Thomson
New Method Multi Robot Team Exploration Policy Autonomous Exploration Mission Space Rover Autonomous Multi Robot Team

May 1, 2024

MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure
Zhicheng Zhang, Yancheng Liang, Yi Wu, Fei Fang
Multi Agent Reinforcement Learning Efficient Exploration Multi Agent Learning State Action Space Exploration Policy Multi Agent Particle Cooperative Multi Agent Learning Mesa Optimization

April 19, 2024

An Off-Policy Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems
Peng Liu, Cong Xu, Ming Zhao, Jiawei Zhu, Bin Wang, Yi Ren
Reinforcement Learning Multi Task Learning Policy Reinforcement Learning Efficient Exploration Exploration Policy Large Scale Recommender System Multi Task Fusion

February 22, 2024

A Landmark-Aware Visual Navigation Dataset
Faith Johnson, Bryan Bo Cao, Kristin Dana, Shubham Jain, Ashwin Ashok
Exploration Policy Visual Navigation Task Waypoint Prediction Landmark Based SLAM

January 17, 2024

UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems
Changshuo Zhang, Sirui Chen, Xiao Zhang, Sunhao Dai, Weijie Yu, Jun Xu
Fine Grained Recommender System User Perception Modern Recommender System Exploration Policy Cumulative Reward

November 30, 2023

Learning active tactile perception through belief-space control
Jean-François Tremblay, David Meger, Francois Hogan, Gregory Dudek
Exploration Policy Height Estimation Generative World Model Differentiable Particle Filter Belief Space Planning Real Robotic System Active Visuo Tactile

November 5, 2023

Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration
Archith Athrey, Othmane Mazhar, Meichen Guo, Bart De Schutter, Shengling Shi
Learning Based Regret Analysis Exploration Policy Binary Forward Exploration Linear Gaussian Gaussian Input

July 10, 2023

Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data
Ruiqi Zhang, Andrea Zanette
Reinforcement Learning Product Design Environment Exploration Optical Experiment Offline Data Provable Guarantee Diverse Policy Exploration Policy Policy Fine Tuning

July 6, 2023

Push Past Green: Learning to Look Behind Plant Foliage by Moving It
Xiaoyu Zhang, Saurabh Gupta
LeArning Abstract Self Supervision Dynamic Model Plant Leaf Exploration Policy Low Visibility Push Forward

July 5, 2023

First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs
Ben Norman, Jeff Clune
Exploration Problem Exploration Policy Exploration Strategy Intelligent Exploration

June 26, 2023

Maximum State Entropy Exploration using Predecessor and Successor Representations
Arnav Kumar Jain, Lucas Lehnert, Irina Rish, Glen Berseth
Successor Representation Exploration Policy Reliable Learning Exploration Algorithm Random Exploration Entropy Exploration Novel Predecessor and Successor

February 15, 2023

Meta-Reinforcement Learning via Exploratory Task Clustering
Zhendong Chu, Hongning Wang
Meta Reinforcement Learning Task Distribution Exploration Policy Sample Efficient Policy Task Clustering

October 7, 2022

Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach
Tomás Delgado, Marco Sánchez Sorondo, Víctor Braberman, Sebastián Uchitel
Reinforcement Learning Reinforcement Learning Approach Model Based Planning Controller Synthesis Exploration Policy

May 30, 2022

SEREN: Knowing When to Explore and When to Exploit
Changmin Yu, David Mguni, Dong Li, Aivar Sootla, Jun Wang, Neil Burgess
Reinforcement Learning Algorithm Efficient Reinforcement Learning Exploration Exploitation Trade Exploration Policy

May 24, 2022

Meta Policy Learning for Cold-Start Conversational Recommendation
Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long, Lingfei Wu
Conversational Recommendation Policy Adaptation Conversational Recommender System Exploration Policy Meta Reinforcement

March 28, 2022

FedVLN: Privacy-preserving Federated Vision-and-Language Navigation
Kaiwen Zhou, Xin Eric Wang
Vision and Language Navigation Unseen Environment Exploration Policy

February 26, 2022

Safe Exploration for Efficient Policy Evaluation and Comparison
Runzhe Wan, Branislav Kveton, Rui Song
Consistent Comparison Policy Evaluation Data Collection Safe Exploration High Quality Data Exploration Policy

Exploration Policy

Papers

Deterministic Exploration via Stationary Bellman Error Maximization

Walk Wisely on Graph: Knowledge Graph Reasoning with Dual Agents via Efficient Guidance-Exploration

Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization

A Novel Methodology for Autonomous Planetary Exploration Using Multi-Robot Teams

MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure

An Off-Policy Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems

A Landmark-Aware Visual Navigation Dataset

UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems

Learning active tactile perception through belief-space control

Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration

Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data

Push Past Green: Learning to Look Behind Plant Foliage by Moving It

First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs

Maximum State Entropy Exploration using Predecessor and Successor Representations

Meta-Reinforcement Learning via Exploratory Task Clustering

Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach

SEREN: Knowing When to Explore and When to Exploit

Meta Policy Learning for Cold-Start Conversational Recommendation

FedVLN: Privacy-preserving Federated Vision-and-Language Navigation

Safe Exploration for Efficient Policy Evaluation and Comparison