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

December 3, 2024

Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents
Ankita Samaddar, Nicholas Potteiger, Xenofon Koutsoukos
Reinforcement Learning Adversarial Attack Distribution Detection Autonomous Agent Intelligent Agent Autonomous Cyber
Generating Critical Scenarios for Testing Automated Driving Systems
Trung-Hieu Nguyen, Truong-Giang Vuong, Hong-Nam Duong, Son Nguyen, Hieu Dinh Vo, Toshiaki Aoki, Thu-Trang Nguyen
Reinforcement Learning Autonomous Driving Autonomous Vehicle Simulation Environment Critical Scenario Generation
Cooperative Cruising: Reinforcement Learning based Time-Headway Control for Increased Traffic Efficiency
Yaron Veksler, Sharon Hornstein, Han Wang, Maria Laura Delle Monache, Daniel Urieli
Reinforcement Learning Traffic Management Adaptive Cruise Control Connected Autonomous Vehicle Cooperative Transportation Traffic Rule Vehicle to Infrastructure Real Time Traffic
Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
Jeongwoo Jae, Jeonghoon Hong, Jinho Choo, Yeong-Dae Kwon
Reinforcement Learning Quantum Machine Learning Quantum State Scaling Limit Quantum Control Quantum Optimization
Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning
Lillian Muyama, Estelle Lu, Geoffrey Cheminet, Jacques Pouchot, Bastien Rance, Anne-Isabelle Tropeano, Antoine Neuraz, Adrien Coulet
Reinforcement Learning Deep Reinforcement Learning Raw Data Real World Clinical Practice Guideline Diagnostic Reasoning Step by Step Accurate Diagnosis
Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum
Maximilian Schenke, Shalbus Bukarov
Reinforcement Learning Optimal Control Technical Report Reinforcement Learning Framework Inverted Pendulum
Selective Reviews of Bandit Problems in AI via a Statistical View
Pengjie Zhou, Haoyu Wei, Huiming Zhang
Reinforcement Learning Artificial Intelligence Multi Armed Bandit Contextual Bandit Bandit Problem Statistical Task Continuum Armed Bandit

December 2, 2024

Learning Ensembles of Vision-based Safety Control Filters
Ihab Tabbara, Hussein Sibai
Reinforcement Learning Training Data Distribution Generalization Vision Based Ensemble Machine Learning
Towards Type Agnostic Cyber Defense Agents
Erick Galinkin, Emmanouil Pountrourakis, Spiros Mancoridis
Reinforcement Learning Artificial Intelligence Cybersecurity Perspective Cyber Defense Bayesian Game
Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic Perspective
Jinouwen Zhang, Rongkun Xue, Yazhe Niu, Yun Chen, Jing Yang, Hongsheng Li, Yu Liu
Reinforcement Learning Generative Training Algorithmic Perspective
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations
Cevahir Koprulu, Po-han Li, Tianyu Qiu, Ruihan Zhao, Tyler Westenbroek, David Fridovich-Keil, Sandeep Chinchali, Ufuk Topcu
Reinforcement Learning Noisy Demonstration Reward Shaping Task Specific Prior
A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication
Homa Nikbakht, Michèle Wigger, Shlomo Shamai (Shitz), H. Vincent Poor
Reinforcement Learning Deep Reinforcement Learning Timely Communication State Space Human Sensing Integrated Sensing Integrated Sensing and Communication

December 1, 2024

Provable Partially Observable Reinforcement Learning with Privileged Information
Yang Cai, Xiangyu Liu, Argyris Oikonomou, Kaiqing Zhang
Reinforcement Learning Multi Agent Reinforcement Learning Full Information Partial Observability Privileged Feature Asymmetric Actor Critic
Hierarchical Prompt Decision Transformer: Improving Few-Shot Policy Generalization with Global and Adaptive Guidance
Zhe Wang, Haozhu Wang, Yanjun Qi
Reinforcement Learning Strong Generalization Adaptive Importance World Event Decision Transformer Text Guidance Context Aware Navigation Shot Support
Linear Probe Penalties Reduce LLM Sycophancy
Henry Papadatos, Rachel Freedman
Large Language Model Reinforcement Learning LLM Generated Linear Probing RLHF V
Bilinear Convolution Decomposition for Causal RL Interpretability
Narmeen Oozeer, Sinem Erisken, Alice Rigg
Reinforcement Learning Convolution Layer Total Correlation
Online Poisoning Attack Against Reinforcement Learning under Black-box Environments
Jianhui Li, Bokang Zhang, Junfeng Wu
Reinforcement Learning Stochastic Gradient Descent Black Box Poisoning Attack Environment Feature Maze Environment Penalty Method Poisoning Efficiency
Decision Transformer vs. Decision Mamba: Analysing the Complexity of Sequential Decision Making in Atari Games
Ke Yan
Reinforcement Learning Action Space Complexity Matter Mamba in Mamba Decision Transformer Sequential Decision Making Reinforcement Learning Model ATARI Game Game Model

November 30, 2024

Learning Dynamic Weight Adjustment for Spatial-Temporal Trajectory Planning in Crowd Navigation
Muqing Cao, Xinhang Xu, Yizhuo Yang, Jianping Li, Tongxing Jin, Pengfei Wang, Tzu-Yi Hung, Guosheng Lin, Lihua Xie
Reinforcement Learning Task Planning Spatio tempOral Obstacle Rich Environment Crowd Navigation Motion Optimization Weight Adjustment Trajectory Planning Algorithm
Towards Fault Tolerance in Multi-Agent Reinforcement Learning
Yuchen Shi, Huaxin Pei, Liang Feng, Yi Zhang, Danya Yao
Reinforcement Learning Multi Agent Reinforcement Learning Fault Tolerance MARL Algorithm Agent Failure

Reinforcement Learning

Papers

Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents

Generating Critical Scenarios for Testing Automated Driving Systems

Cooperative Cruising: Reinforcement Learning based Time-Headway Control for Increased Traffic Efficiency

Reinforcement learning to learn quantum states for Heisenberg scaling accuracy

Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning

Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum

Selective Reviews of Bandit Problems in AI via a Statistical View

Learning Ensembles of Vision-based Safety Control Filters

Towards Type Agnostic Cyber Defense Agents

Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic Perspective

Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations

A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication

Provable Partially Observable Reinforcement Learning with Privileged Information

Hierarchical Prompt Decision Transformer: Improving Few-Shot Policy Generalization with Global and Adaptive Guidance

Linear Probe Penalties Reduce LLM Sycophancy

Bilinear Convolution Decomposition for Causal RL Interpretability

Online Poisoning Attack Against Reinforcement Learning under Black-box Environments

Decision Transformer vs. Decision Mamba: Analysing the Complexity of Sequential Decision Making in Atari Games

Learning Dynamic Weight Adjustment for Spatial-Temporal Trajectory Planning in Crowd Navigation

Towards Fault Tolerance in Multi-Agent Reinforcement Learning