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
BAMAX: Backtrack Assisted Multi-Agent Exploration using Reinforcement Learning
Geetansh Kalra, Amit Patel, Atul Chaudhari, Divye Singh
RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm
Geetansh Kalra, Divye Singh, Justin Jose
R3HF: Reward Redistribution for Enhancing Reinforcement Learning from Human Feedback
Jiahui Li, Tai-wei Chang, Fengda Zhang, Kun Kuang, Long Chen
Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning
Stefan Pranger, Hana Chockler, Martin Tappler, Bettina Könighofer
Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Scheduling
Maria Zampella, Urtzi Otamendi, Xabier Belaunzaran, Arkaitz Artetxe, Igor G. Olaizola, Giuseppe Longo, Basilio Sierra
Entropy Controllable Direct Preference Optimization
Motoki Omura, Yasuhiro Fujita, Toshiki Kataoka
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
Chenbei Lu, Laixi Shi, Zaiwei Chen, Chenye Wu, Adam Wierman
Reinforcement Learning Framework for Quantitative Trading
Alhassan S. Yasin, Prabdeep S. Gill
SynRL: Aligning Synthetic Clinical Trial Data with Human-preferred Clinical Endpoints Using Reinforcement Learning
Trisha Das, Zifeng Wang, Afrah Shafquat, Mandis Beigi, Jason Mezey, Jimeng Sun
To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
Maddalena Boscaro, Federico Mason, Federico Chiariotti, Andrea Zanella
Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of Mind
Antonio Andriella, Giovanni Falcone, Silvia Rossi
Multi-hop Upstream Preemptive Traffic Signal Control with Deep Reinforcement Learning
Xiaocan Li, Xiaoyu Wang, Ilia Smirnov, Scott Sanner, Baher Abdulhai
RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
Boyao Wang, Volodymyr Kindratenko
Reinforcement learning for Quantum Tiq-Taq-Toe
Catalin-Viorel Dinu, Thomas Moerland
Do you want to play a game? Learning to play Tic-Tac-Toe in Hypermedia Environments
Katharine Beaumont, Rem Collier
Optimal Execution with Reinforcement Learning
Yadh Hafsi, Edoardo Vittori
State Chrono Representation for Enhancing Generalization in Reinforcement Learning
Jianda Chen, Wen Zheng Terence Ng, Zichen Chen, Sinno Jialin Pan, Tianwei Zhang
Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout
Keqin Li, Lipeng Liu, Jiajing Chen, Dezhi Yu, Xiaofan Zhou, Ming Li, Congyu Wang, Zhao Li
CROPS: A Deployable Crop Management System Over All Possible State Availabilities
Jing Wu, Zhixin Lai, Shengjie Liu, Suiyao Chen, Ran Tao, Pan Zhao, Chuyuan Tao, Yikun Cheng, Naira Hovakimyan