Reinforcement Learning Problem

Reinforcement learning (RL) focuses on training agents to make optimal sequential decisions by learning from interactions with an environment. Current research emphasizes improving algorithm efficiency and robustness, particularly through advancements in model architectures like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), and addressing challenges such as constrained Markov decision processes (CMDPs) and non-stationary environments. RL's significance stems from its broad applicability, ranging from autonomous driving and sustainable energy management to optimizing industrial processes like inventory control and even architectural design, promising significant improvements in efficiency and decision-making across diverse fields.

Papers

October 28, 2024

Offline Reinforcement Learning With Combinatorial Action Spaces
Matthew Landers, Taylor W. Killian, Hugo Barnes, Thomas Hartvigsen, Afsaneh Doryab
Offline Reinforcement Learning Action Space Reinforcement Learning Problem Branch Prediction

October 18, 2024

Inverse Reinforcement Learning from Non-Stationary Learning Agents
Kavinayan P. Sivakumar, Yi Shen, Zachary Bell, Scott Nivison, Boyuan Chen, Michael M. Zavlanos
Inverse Reinforcement Learning Behavior Cloning Non Stationary Reinforcement Learning Problem

September 25, 2024

Topological Foundations of Reinforcement Learning
David Krame Kadurha
Reinforcement Learning Markov Decision Process Reinforcement Learning Problem Compression Operator

August 6, 2024

Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning
Zixiang Wang, Hao Yan, Changsong Wei, Junyu Wang, Shi Bo, Minheng Xiao
Deep Reinforcement Learning Autonomous Driving DH Research Deep Q Network Reinforcement Learning Problem Vehicle Decision Making

July 26, 2024

Reinforcement Learning for Sustainable Energy: A Survey
Koen Ponse, Felix Kleuker, Márton Fejér, Álvaro Serra-Gómez, Aske Plaat, Thomas Moerland
Reinforcement Learning Timely Survey Safe Reinforcement Learning Reinforcement Learning Problem Sustainable Energy

February 26, 2024

Achieving $\tilde{O}(1/\epsilon)$ Sample Complexity for Constrained Markov Decision Process
Jiashuo Jiang, Yinyu Ye
Sample Complexity Sequential Learning Constrained Markov Decision Process Reinforcement Learning Problem Logarithmic Regret Optimal Guarantee

February 7, 2024

Learning Communication Policies for Different Follower Behaviors in a Collaborative Reference Game
Philipp Sadler, Sherzod Hakimov, David Schlangen
Reinforcement Learning Reinforcement Learning Problem Neural Agent Referential Game Communication Learning

February 4, 2024

Accelerating Inverse Reinforcement Learning with Expert Bootstrapping
David Wu, Sanjiban Choudhury
Reinforcement Learning Imitation Learning Inverse Reinforcement Learning Bootstrapping End to End Reinforcement Learning Problem

January 31, 2024

Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming
Haotian Ling, Zhihai Wang, Jie Wang
LeArning Abstract Mixed Integer Reinforcement Learning Problem Mixed Integer Linear Dynamic Decision Cutting Plane MILP Solver

December 19, 2023

ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Models Inference
Ziqian Zeng, Yihuai Hong, Hongliang Dai, Huiping Zhuang, Cen Chen
Scientific Inference Strong Consistency Efficient Inference Early Exit Reinforcement Learning Problem Language Model Inference

December 12, 2023

I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives
R. Teal Witter, Lucas Rosenblatt
Reinforcement Learning Graph Neural Network Q Learning Reinforcement Learning Problem Open Street

November 5, 2023

Staged Reinforcement Learning for Complex Tasks through Decomposed Environments
Rafael Pina, Corentin Artaud, Xiaolan Liu, Varuna De Silva
Reinforcement Learning Multi Agent Microbial Decomposition Complex Task Reinforcement Learning Problem

September 6, 2023

Near-continuous time Reinforcement Learning for continuous state-action spaces
Lorenzo Croissant, Marc Abeille, Bruno Bouchard
Reinforcement Learning Reward Function Action Space State Action Space Reinforcement Learning Problem Linear Time Structured State Evolution

August 8, 2023

Scope Loss for Imbalanced Classification and RL Exploration
Hasham Burhani, Xiao Qi Shi, Jonathan Jaegerman, Daniel Balicki
Reinforcement Learning Loss Function Reinforcement Learning Benchmark Imbalanced Classification Scope Sensitive Reinforcement Learning Problem

July 20, 2023

July 13, 2023

The complexity of non-stationary reinforcement learning
Christos Papadimitriou, Binghui Peng
Reinforcement Learning Continual LEArning Complexity Matter State Action Pair Reinforcement Learning Problem Non Stationary Reinforcement Learning

July 3, 2023

Achieving Stable Training of Reinforcement Learning Agents in Bimodal Environments through Batch Learning
E. Hurwitz, N. Peace, G. Cevora
Reinforcement Learning Reinforcement Learning Agent Stochastic Environment Reinforcement Learning Problem Stable Training Batch Learning Tabular Q Learning Multimodal Environment

June 26, 2023

On Imitation in Mean-field Games
Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist
Feature Imitation Mean Field Mean Field Game Reinforcement Learning Problem Nash Equilibrium Policy

May 22, 2023

Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers
Philipp Sadler, Sherzod Hakimov, David Schlangen
Good Teacher New Way Past Action Reinforcement Learning Problem Referential Game Episodic Control Referring Expression Grounding Linguistic Signal