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
On the Perturbed States for Transformed Input-robust Reinforcement Learning
Tung M. Luu, Haeyong Kang, Tri Ton, Thanh Nguyen, Chang D. Yoo
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks
David Valencia, Henry Williams, Yuning Xing, Trevor Gee, Minas Liarokapis, Bruce A. MacDonald
How to Choose a Reinforcement-Learning Algorithm
Fabian Bongratz, Vladimir Golkov, Lukas Mautner, Luca Della Libera, Frederik Heetmeyer, Felix Czaja, Julian Rodemann, Daniel Cremers
ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learning
Hosung Lee, Sejin Kim, Seungpil Lee, Sanha Hwang, Jihwan Lee, Byung-Jun Lee, Sundong Kim
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning
Norman Di Palo, Leonard Hasenclever, Jan Humplik, Arunkumar Byravan
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
Yupei Yang, Biwei Huang, Fan Feng, Xinyue Wang, Shikui Tu, Lei Xu
A Method for Fast Autonomy Transfer in Reinforcement Learning
Dinuka Sahabandu, Bhaskar Ramasubramanian, Michail Alexiou, J. Sukarno Mertoguno, Linda Bushnell, Radha Poovendran
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World
Hari Prasad, Chinnu Jacob, Imthias Ahamed T. P
Collision Probability Distribution Estimation via Temporal Difference Learning
Thomas Steinecker, Thorsten Luettel, Mirko Maehlisch
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning
Leen Kweider, Maissa Abou Kassem, Ubai Sandouk
SOAP-RL: Sequential Option Advantage Propagation for Reinforcement Learning in POMDP Environments
Shu Ishida, João F. Henriques
Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers
David Huergo, Martín de Frutos, Eduardo Jané, Oscar A. Marino, Gonzalo Rubio, Esteban Ferrer
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning
Mostafa Kotb, Cornelius Weber, Muhammad Burhan Hafez, Stefan Wermter
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning
Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White
Reinforcement Learning for Sustainable Energy: A Survey
Koen Ponse, Felix Kleuker, Márton Fejér, Álvaro Serra-Gómez, Aske Plaat, Thomas Moerland
Online Test Synthesis From Requirements: Enhancing Reinforcement Learning with Game Theory
Ocan Sankur, Thierry Jéron, Nicolas Markey, David Mentré, Reiya Noguchi
Non-Overlapping Placement of Macro Cells based on Reinforcement Learning in Chip Design
Tao Yu, Peng Gao, Fei Wang, Ru-Yue Yuan