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

September 6, 2024

RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs
Jiaxing Wu, Lin Ning, Luyang Liu, Harrison Lee, Neo Wu, Chao Wang, Sushant Prakash, Shawn O'Banion, Bradley Green, Jun Xie
Large Language Model Reinforcement Learning LLM Personalization Personalized Summarization
Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization
Minh Vu, Konstantinos Slavakis
Reinforcement Learning Loss Function Deep Q Network Riemannian Optimization Function Estimation
Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering
Jan Hofmann, Cornelia Sindermann, Roman Klinger
Reinforcement Learning Relevance Modeling Authorship Analysis Personality Profiling User Profiling Relevance Aware Twitter Corpus

September 5, 2024

Asynchronous Stochastic Approximation and Average-Reward Reinforcement Learning
Huizhen Yu, Yi Wan, Richard S. Sutton
Reinforcement Learning Average Reward Reinforcement Learning Asynchronous Stochastic Semi Markov Decision Process
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
Muxing Wang, Pengkun Yang, Lili Su
Reinforcement Learning Q Learning Convergence Rate Federated Prompt Cooperation Q Function Large Scale Multi Agent Heterogeneous Environment Slow Convergence
Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron
Christian Schmid, James M. Murray
Reinforcement Learning Neural Network Learning Dynamic Learning Rule Elementary Perceptron
PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization
Federico Berto, Chuanbo Hua, Laurin Luttmann, Jiwoo Son, Junyoung Park, Kyuree Ahn, Changhyun Kwon, Lin Xie, Jinkyoo Park
Reinforcement Learning Multi Agent
Simplex-enabled Safe Continual Learning Machine
Hongpeng Cao, Yanbing Mao, Yihao Cai, Lui Sha, Marco Caccamo
Reinforcement Learning Safe Learning Critical Autonomous System
Game On: Towards Language Models as RL Experimenters
Jingwei Zhang, Thomas Lampe, Abbas Abdolmaleki, Jost Tobias Springenberg, Martin Riedmiller
Language Model Reinforcement Learning Robotics Domain Game Content Language Conditioned Agent Architecture
ELO-Rated Sequence Rewards: Advancing Reinforcement Learning Models
Qi Ju, Falin Hei, Zhemei Fang, Yunfeng Luo
Reinforcement Learning Deep Learning Reinforcement Learning Model Utility Based Learning Reward Reward Redistribution
In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Search
Emir Demirović, Christian Schilling, Anna Lukina
Reinforcement Learning Decision Tree Black Box Search Query Tree Specie Neural Network Policy Decision Tree Policy
E2CL: Exploration-based Error Correction Learning for Embodied Agents
Hanlin Wang, Chak Tou Leong, Jian Wang, Wenjie Li
Reinforcement Learning LLM Agent Embodied Agent Learning With Error
InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management
Pranay Thangeda, Trevor S. Betz, Michael N. Grussing, Melkior Ornik
Reinforcement Learning Decision Making Transportation Infrastructure
Autonomous Drifting Based on Maximal Safety Probability Learning
Hikaru Hoshino, Jiaxing Li, Arnav Menon, John M. Dolan, Yorie Nakahira
Reinforcement Learning Autonomous Driving Reward Shaping Safety Guarantee Probabilistic Safety Autonomous Drifting

September 4, 2024

RoboKoop: Efficient Control Conditioned Representations from Visual Input in Robotics using Koopman Operator
Hemant Kumawat, Biswadeep Chakraborty, Saibal Mukhopadhyay
Reinforcement Learning Robotics Domain Visual Representation Koopman Operator Visual Input Efficient Control
Tractable Offline Learning of Regular Decision Processes
Ahana Deb, Roberto Cipollone, Anders Jonsson, Alessandro Ronca, Mohammad Sadegh Talebi
Reinforcement Learning Offline Reinforcement Learning Algorithm Non Markovian Finite State Automaton
Surgical Task Automation Using Actor-Critic Frameworks and Self-Supervised Imitation Learning
Jingshuai Liu, Alain Andres, Yonghang Jiang, Xichun Luo, Wenmiao Shu, Sotirios Tsaftaris
Reinforcement Learning Imitation Learning Actor Critic Expert Demonstration Actor Critic Reinforcement Learning Surgical Automation
A Survey on Emergent Language
Jannik Peters, Constantin Waubert de Puiseau, Hasan Tercan, Arya Gopikrishnan, Gustavo Adolpho Lucas De Carvalho, Christian Bitter, Tobias Meisen
Reinforcement Learning Timely Survey Multi Agent Reinforcement Learning Emergent Language
Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal
Jifeng Hu, Li Shen, Sili Huang, Zhejian Yang, Hechang Chen, Lichao Sun, Yi Chang, Dacheng Tao
Reinforcement Learning Diffusion Based Model Stability Plasticity Multi View Diffuser Offline Visual Representation Learning
Large Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Learning
Guanwen Xie, Jingzehua Xu, Yiyuan Yang, Yimian Ding, Shuai Zhang
Reinforcement Learning Reward Function Multi Objective Reinforcement Learning Reward Learning Task Specific Reward

Reinforcement Learning

Papers

RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs

Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization

Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering

Asynchronous Stochastic Approximation and Average-Reward Reinforcement Learning

On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments

Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron

PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization

Simplex-enabled Safe Continual Learning Machine

Game On: Towards Language Models as RL Experimenters

ELO-Rated Sequence Rewards: Advancing Reinforcement Learning Models

In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Search

E2CL: Exploration-based Error Correction Learning for Embodied Agents

InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management

Autonomous Drifting Based on Maximal Safety Probability Learning

RoboKoop: Efficient Control Conditioned Representations from Visual Input in Robotics using Koopman Operator

Tractable Offline Learning of Regular Decision Processes

Surgical Task Automation Using Actor-Critic Frameworks and Self-Supervised Imitation Learning

A Survey on Emergent Language

Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal

Large Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Learning