Multi Agent Problem

Multi-agent problems involve coordinating the actions of multiple independent agents to achieve a shared goal, posing significant challenges due to the complexity of interactions and decentralized decision-making. Current research focuses on developing scalable algorithms, such as hierarchical reinforcement learning and transformer-based approaches, to address these challenges, often incorporating spatial information and addressing issues like sparse rewards and temporal dependencies. These advancements are crucial for improving the performance of autonomous systems in diverse applications, including robotics, traffic control, and resource management, by enabling more efficient and robust collaboration among agents.

Papers

May 26, 2024

Towards Imitation Learning in Real World Unstructured Social Mini-Games in Pedestrian Crowds
Rohan Chandra, Haresh Karnan, Negar Mehr, Peter Stone, Joydeep Biswas
Imitation Learning Motion Planning Multi Agent Interaction Multi Agent Problem

March 26, 2024

Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph
Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, Xiaolin Ai
Multi Agent Reinforcement Learning Multi Agent Problem Self Supervised Clustering Cooperation Graph

March 23, 2024

Multi-agent transformer-accelerated RL for satisfaction of STL specifications
Albin Larsson Forsberg, Alexandros Nikou, Aneta Vulgarakis Feljan, Jana Tumova
Reinforcement Learning Multi Agent Reinforcement Learning Potential Scalability Centralized Training User Satisfaction Multi Agent Problem Transformer Agent

September 29, 2023

Multi-Objective Sparse Sensing with Ergodic Optimization
Ananya Rao, Howie Choset
Multi Objective Agent Team Sparse Measurement Multi Agent Problem

September 19, 2023

Multicopy Reinforcement Learning Agents
Alicia P. Wolfe, Oliver Diamond, Brigitte Goeler-Slough, Remi Feuerman, Magdalena Kisielinska, Victoria Manfredi
Single Agent Near Duplicate Multi Agent Problem Multi Agent Reinforcement Learning Environment

April 25, 2023

SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning
Dapeng Li, Zhiwei Xu, Bin Zhang, Guoliang Fan
Multi Agent Reinforcement Learning Information Extraction Upper Ocean Spatial Information Multi Agent Scenario Multi Agent Problem Explicit Construction

November 22, 2022

OLGA : An Ontology and LSTM-based approach for generating Arithmetic Word Problems (AWPs) of transfer type
Suresh Kumar, P Sreenivasa Kumar
Long Short Term Memory Constructive Approach Top Level Ontology LSTM Network Math Word Problem Multi Agent Problem Word Problem

November 9, 2022

Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics
Nitsan Soffair
Reinforcement Learning Markov Decision Process Single Agent Multi Agent Problem

September 18, 2022

Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
Hua Wei, Jingxiao Chen, Xiyang Ji, Hongyang Qin, Minwen Deng, Siqin Li, Liang Wang, Weinan Zhang, Yong Yu, Lin Liu, Lanxiao Huang, Deheng Ye, Qiang Fu, Wei Yang
Reinforcement Learning Strong Generalization Environment Feature Generalization Problem Arena Hard Multi Agent Problem

March 6, 2022

Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment
Diogo S. Carvalho, Biswa Sengupta
Reinforcement Learning Deep Reinforcement Learning New Task Multi Agent Environment Efficient Scheduling Multi Agent Problem Intelligent Warehouse

November 23, 2021

Status-quo policy gradient in Multi-Agent Reinforcement Learning
Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri, Jayakumar Subramanian, Abhishek Sinha, Siddharth Singh, Balaji Krishnamurthy
Reinforcement Learning Multi Agent Reinforcement Learning Policy Gradient Matrix Game Multi Agent Problem

November 22, 2021

Off-Policy Correction For Multi-Agent Reinforcement Learning
Michał Zawalski, Błażej Osiński, Henryk Michalewski, Piotr Miłoś
Multi Agent Reinforcement Learning Multi Agent Actor Critic Algorithm Multi Agent Challenge Multi Agent Problem