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
Superior Computer Chess with Model Predictive Control, Reinforcement Learning, and Rollout
Atharva Gundawar, Yuchao Li, Dimitri Bertsekas
Learning Generative Interactive Environments By Trained Agent Exploration
Naser Kazemi, Nedko Savov, Danda Paudel, Luc Van Gool
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning
Shreyas S R
Learning Augmentation Policies from A Model Zoo for Time Series Forecasting
Haochen Yuan, Xuelin Li, Yunbo Wang, Xiaokang Yang
An Introduction to Quantum Reinforcement Learning (QRL)
Samuel Yen-Chi Chen
Cooperative Decision-Making for CAVs at Unsignalized Intersections: A MARL Approach with Attention and Hierarchical Game Priors
Jiaqi Liu, Peng Hang, Xiaoxiang Na, Chao Huang, Jian Sun
BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design
Yannick Vogt, Mehdi Naouar, Maria Kalweit, Christoph Cornelius Miething, Justus Duyster, Joschka Boedecker, Gabriel Kalweit
Fair Reinforcement Learning Algorithm for PV Active Control in LV Distribution Networks
Maurizio Vassallo, Amina Benzerga, Alireza Bahmanyar, Damien Ernst
Reinforcement Learning for Variational Quantum Circuits Design
Simone Foderà , Gloria Turati, Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi
Semifactual Explanations for Reinforcement Learning
Jasmina Gajcin, Jovan Jeromela, Ivana Dusparic
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping
Aly Lidayan, Michael Dennis, Stuart Russell
GOPT: Generalizable Online 3D Bin Packing via Transformer-based Deep Reinforcement Learning
Heng Xiong, Changrong Guo, Jian Peng, Kai Ding, Wenjie Chen, Xuchong Qiu, Long Bai, Jianfeng Xu
Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging
Mingyan Zhou, Biao Wang, Tian Tan, Xiatao Sun
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
Hongyao Tang, Glen Berseth
LMGT: Optimizing Exploration-Exploitation Balance in Reinforcement Learning through Language Model Guided Trade-offs
Yongxin Deng, Xihe Qiu, Xiaoyu Tan, Wei Chu, Yinghui Xu
Causality-Driven Reinforcement Learning for Joint Communication and Sensing
Anik Roy, Serene Banerjee, Jishnu Sadasivan, Arnab Sarkar, Soumyajit Dey