Reinforcement Learning Agent

Reinforcement learning (RL) agents are computational systems designed to learn optimal decision-making strategies through trial and error, aiming to maximize cumulative rewards within a defined environment. Current research emphasizes improving RL agent efficiency and robustness, focusing on areas like scalable architecture search, offline training with real-world and simulated data, and incorporating safety mechanisms and ethical considerations into agent design. These advancements are significant for diverse applications, including building energy optimization, personalized recommendations, and autonomous systems, driving progress in both theoretical understanding and practical deployment of RL.

Papers

March 5, 2024

Language Guided Exploration for RL Agents in Text Environments
Hitesh Golchha, Sahil Yerawar, Dhruvesh Patel, Soham Dan, Keerthiram Murugesan
Reinforcement Learning Agent Language Navigation Sequential Decision Making Decision Support Tabular Reinforcement Learning Text Based Environment

February 21, 2024

Enhancing Reinforcement Learning Agents with Local Guides
Paul Daoudi, Bogdan Robu, Christophe Prieur, Ludovic Dos Santos, Merwan Barlier
Reinforcement Learning Reinforcement Learning Agent Approximate Policy Iteration

February 6, 2024

Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
Yash Shukla, Tanushree Burman, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov
Reinforcement Learning Temporal Logic Reinforcement Learning Agent Compositional Reinforcement Learning Task Sampling Observable Robotic

February 2, 2024

Brain-Like Replay Naturally Emerges in Reinforcement Learning Agents
Jiyi Wang, Likai Tang, Huimiao Chen, Marcelo G Mattar, Sen Song
Reinforcement Learning Reinforcement Learning Agent Episodic Memory Activation Pattern Nonlocal Model Cognitive Replay

January 21, 2024

Emergent Dominance Hierarchies in Reinforcement Learning Agents
Ram Rachum, Yonatan Nakar, Bill Tomlinson, Nitay Alon, Reuth Mirsky
Multi Agent Reinforcement Learning Reinforcement Learning Agent Intrinsic Reward Dominance Hierarchy Modern Reinforcement Learning

January 18, 2024

FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction
Alexander Telepov, Artem Tsypin, Kuzma Khrabrov, Sergey Yakukhnov, Pavel Strashnov, Petr Zhilyaev, Egor Rumiantsev, Daniel Ezhov, Manvel Avetisian, Olga Popova, Artur Kadurin
Reinforcement Learning Reinforcement Learning Agent Molecule Generation Molecular Docking Protein Protein Interaction Serial Reproduction

December 24, 2023

Conservative Exploration for Policy Optimization via Off-Policy Policy Evaluation
Paul Daoudi, Mathias Formoso, Othman Gaizi, Achraf Azize, Evrard Garcelon
Policy OpTimization Policy Evaluation Reinforcement Learning Agent Finite Horizon Conservative Constraint Conservative Exploration

December 23, 2023

Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling
Xianjie Zhang, Jiahao Sun, Chen Gong, Kai Wang, Yifei Cao, Hao Chen, Hao Chen, Yu Liu
Mutual Information Reinforcement Learning Agent Mobility on Demand Ride Pooling Real World Taxi

December 16, 2023

Active Reinforcement Learning for Robust Building Control
Doseok Jang, Larry Yan, Lucas Spangher, Costas Spanos
Reinforcement Learning Reinforcement Learning Agent Robot Control Unsupervised Environment Design Active Reinforcement Learning

December 14, 2023

Personalized Path Recourse for Reinforcement Learning Agents
Dat Hong, Tong Wang
Reinforcement Learning Reinforcement Learning Agent Algorithmic Recourse

December 7, 2023

CODEX: A Cluster-Based Method for Explainable Reinforcement Learning
Timothy K. Mathes, Jessica Inman, Andrés Colón, Simon Khan
Reinforcement Learning Counterfactual Explanation Reinforcement Learning Agent Agent Behavior State Action Space Explainable Reinforcement Learning Clustering Based Approach Game Representation

December 5, 2023

LExCI: A Framework for Reinforcement Learning with Embedded Systems
Kevin Badalian, Lucas Koch, Tobias Brinkmann, Mario Picerno, Marius Wegener, Sung-Yong Lee, Jakob Andert
Reinforcement Learning Artificial Intelligence New Framework Reinforcement Learning Agent Embedded System Reinforcement Learning Library

December 4, 2023

November 22, 2023

Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
Zihao Zhou, Bin Hu, Chenyang Zhao, Pu Zhang, Bin Liu
Large Language Model Reinforcement Learning Agent LLM Based Agent Based Policy

November 8, 2023

Fuzzy Ensembles of Reinforcement Learning Policies for Robotic Systems with Varied Parameters
Abdel Gafoor Haddad, Mohammed B. Mohiuddin, Igor Boiko, Yahya Zweiri
Reinforcement Learning Robotic System Many Parameter Reinforcement Learning Agent Parallel Training Classical Control Ensemble Aggregation

October 25, 2023

Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop
Mario Picerno, Lucas Koch, Kevin Badalian, Marius Wegener, Joschka Schaub, Charles Robert Koch, Jakob Andert
Reinforcement Learning Full Model Formality Transfer Reinforcement Learning Agent Positive Reinforcement M$ Base Controller Hardware in the Loop Pre Trained Reinforcement Learning

October 14, 2023

LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents
Yash Shukla, Wenchang Gao, Vasanth Sarathy, Alvaro Velasquez, Robert Wright, Jivko Sinapov
Reinforcement Learning Task Planning Reinforcement Learning Agent Fine Tuned Large Language Model Semi Supervised Teacher Student Task Sampling

October 13, 2023

Automatic Music Playlist Generation via Simulation-based Reinforcement Learning
Federico Tomasi, Joseph Cauteruccio, Surya Kanoria, Kamil Ciosek, Matteo Rinaldi, Zhenwen Dai
Reinforcement Learning Reinforcement Learning Agent User Satisfaction Playlist Generation

September 27, 2023

DTC: Deep Tracking Control
Fabian Jenelten, Junzhe He, Farbod Farshidian, Marco Hutter
Trajectory Optimization Reinforcement Learning Agent Legged Locomotion Deep Reinforcement Learning Based Legged System