Reinforcement Learning

Reinforcement learning (RL) focuses on training agents to make optimal decisions in an environment by learning through trial and error, aiming to maximize cumulative rewards. Current research emphasizes improving RL's efficiency and robustness, particularly in areas like human-in-the-loop training (e.g., using human feedback to refine models), handling uncertainty and sparse rewards, and scaling to complex tasks (e.g., robotics, autonomous driving). Prominent approaches involve various policy gradient methods, Monte Carlo Tree Search, and the integration of large language models for improved decision-making and task decomposition. These advancements are driving progress in diverse fields, including robotics, game playing, and the development of more human-aligned AI systems.

Papers

July 22, 2024

Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN
Norman Becker, Daniel Reti, Evridiki V. Ntagiou, Marcus Wallum, Hans D. Schotten
Reinforcement Learning Q Learning DQN Agent Penetration Testing
Reinforcement Learning Meets Visual Odometry
Nico Messikommer, Giovanni Cioffi, Mathias Gehrig, Davide Scaramuzza
Reinforcement Learning Mobile Robot Visual Odometry 3D Awareness Sequential Decision Making Task
Sustainable broadcasting in Blockchain Network with Reinforcement Learning
Danila Valko, Daniel Kudenko
Reinforcement Learning Blockchain Network Broadcasting Support Relation Broadcast Network
Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications
Sinan Ibrahim, Mostafa Mostafa, Ali Jnadi, Hadi Salloum, Pavel Osinenko
Reinforcement Learning Reward Function Much Progress Reward Shaping Reward Engineering Reinforcement Learning System
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search
Peng Cheng, Huimu Wang, Jinyuan Zhao, Yihao Wang, Enqiang Xu, Yu Zhao, Zhuojian Xiao, Songlin Wang, Guoyu Tang, Lin Liu, Sulong Xu
Reinforcement Learning E Commerce Search Multi Objective Deep Reinforcement Learning Traffic Allocation

July 21, 2024

Temporal Abstraction in Reinforcement Learning with Offline Data
Ranga Shaarad Ayyagari, Anurita Ghosh, Ambedkar Dukkipati
Reinforcement Learning Hierarchical Reinforcement Learning Offline Data Multi Agent MuJoCo Standard Reinforcement Learning Temporal Abstraction
Large Language Model for Verilog Generation with Golden Code Feedback
Ning Wang, Bingkun Yao, Jie Zhou, Xi Wang, Zhe Jiang, Nan Guan
Large Language Model Reinforcement Learning Pre Trained Model Natural Language Instruction Verilog Code Generation Feedback Code
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning
Yuxuan Jiang, Yujie Yang, Zhiqian Lan, Guojian Zhan, Shengbo Eben Li, Qi Sun, Jian Ma, Tianwen Yu, Changwu Zhang
Reinforcement Learning Underactuated System Precision Landing Exploration Characteristic

July 20, 2024

VisFly: An Efficient and Versatile Simulator for Training Vision-based Flight
Fanxing Li, Fangyu Sun, Tianbao Zhang, Danping Zou
Reinforcement Learning High Efficiency Simulation Framework Scene Datasets Agile Flight Quadrotor Model
Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming
Manuel Eberhardinger, Florian Rupp, Johannes Maucher, Setareh Maghsudi
Reinforcement Learning Machine Learning Domain Specific Genetic Programming Decision Making Process Unpredictable Action

July 19, 2024

A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning
Alejandro L. García Navarro, Nataliia Koneva, Alfonso Sánchez-Macián, José Alberto Hernández
Reinforcement Learning Machine Learning Data Science Scikit Learn SPR Package OpenAI Gym
BOND: Aligning LLMs with Best-of-N Distillation
Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Nino Vieillard, Alexandre Ramé, Bobak Shariari, Sarah Perrin, Abe Friesen, Geoffrey Cideron, Sertan Girgin, Piotr Stanczyk, Andrea Michi, Danila Sinopalnikov, Sabela Ramos, Amélie Héliou, Aliaksei Severyn, Matt Hoffman, Nikola Momchev, Olivier Bachem
Reinforcement Learning Medical LLM Reinforcement Learning From Human Feedback Distillation Framework Best of N
Catastrophic Goodhart: regularizing RLHF with KL divergence does not mitigate heavy-tailed reward misspecification
Thomas Kwa, Drake Thomas, Adrià Garriga-Alonso
Reinforcement Learning Optimal Policy Reward Model Reinforcement Learning From Human Feedback Heavy Tailed KL Divergence Heavy Tailed Reward Extinction Risk
Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning
Nihal Acharya Adde, Hanno Gottschalk, Andreas Ebert
Reinforcement Learning Autonomous Driving Hyperparameter Optimization General Strategy Reliable Autonomous Driving Autonomous Driving Strategy
Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment
Jeffrey Redondo, Zhenhui Yuan, Nauman Aslam, Juan Zhang
Reinforcement Learning Multi Agent High Definition Map Coverage Criterion Vehicular Ad Hoc Network Enhanced Network

July 18, 2024

Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications
Nasir Khan, Sinem Coleri
Reinforcement Learning Vehicular Network Ultra Reliable Low Latency Communication
Learning Goal-Conditioned Representations for Language Reward Models
Vaskar Nath, Dylan Slack, Jeff Da, Yuntao Ma, Hugh Zhang, Spencer Whitehead, Sean Hendryx
Reinforcement Learning Reward Model Self Supervised Objective Traditional Reinforcement Learning Goal Conditioned Language Reward Reward Model Training
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization
Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer
Reinforcement Learning Deep Reinforcement Learning Strong Generalization Generalization Performance Instance Selection Algorithm Configuration Low Generalization
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause
Reinforcement Learning Markov Decision Process Complementary Benefit Active Exploration Cognitive Abstraction MDP Homomorphism
LLM-Empowered State Representation for Reinforcement Learning
Boyuan Wang, Yun Qu, Yuhang Jiang, Jianzhun Shao, Chang Liu, Wenming Yang, Xiangyang Ji
Reinforcement Learning State Representation Task Relevant State

Reinforcement Learning

Papers

Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN

Reinforcement Learning Meets Visual Odometry

Sustainable broadcasting in Blockchain Network with Reinforcement Learning

Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications

MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search

Temporal Abstraction in Reinforcement Learning with Offline Data

Large Language Model for Verilog Generation with Golden Code Feedback

Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning

VisFly: An Efficient and Versatile Simulator for Training Vision-based Flight

Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming

A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning

BOND: Aligning LLMs with Best-of-N Distillation

Catastrophic Goodhart: regularizing RLHF with KL divergence does not mitigate heavy-tailed reward misspecification

Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning

Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment

Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications

Learning Goal-Conditioned Representations for Language Reward Models

Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization

Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction

LLM-Empowered State Representation for Reinforcement Learning