AI Agent - arXivDaily 专题

2001.07527 2020-01-22 cs.LG cs.AI cs.MA stat.ML 88%

Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping

Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

1810.11702 2020-01-14 cs.MA cs.AI cs.GT cs.LG 88%

Multi-Agent Common Knowledge Reinforcement Learning

Christian A. Schroeder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments Advances in Neural Information Processing Systems, 9924-9935

1912.03821 2019-12-10 cs.LG cs.AI cs.MA cs.SY eess.SY math.OC stat.ML 88%

Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances

Kaiqing Zhang, Zhuoran Yang, Tamer Başar

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments This is a invited submission to a Special Issue of the Journal of Frontiers of Information Technology & Electronic Engineering (FITEE). Most of the contents are based on the Sec. 4 in our recent overview arXiv:1911.10635, with focus on the setting of decentralized MARL with networked agents

1912.01513 2019-12-04 cs.AI cs.LG cs.MA 88%

BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)

Marek Rosa, Olga Afanasjeva, Simon Andersson, Joseph Davidson, Nicholas Guttenberg, Petr Hlubuček, Martin Poliak, Jaroslav Vítku, Jan Feyereisl

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

1912.00949 2019-12-03 cs.LG cs.AI cs.MA stat.ML 88%

Multi-Agent Deep Reinforcement Learning with Adaptive Policies

Yixiang Wang, Feng Wu

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments arXiv admin note: text overlap with arXiv:1706.02275 by other authors

1910.01465 2019-12-03 cs.LG cs.AI cs.MA stat.ML 88%

Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

Johannes Ackermann, Volker Gabler, Takayuki Osa, Masashi Sugiyama

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted for the Deep RL Workshop at NeurIPS 2019; Changes for v2: Changed Figures 3,4, due to an error in the implementation of MATD3. Please refer to this version for fair evaluation

1912.00303 2019-12-03 cs.MA cs.AI cs.LG 88%

MANELA: A Multi-Agent Algorithm for Learning Network Embeddings

Han Zhang, Hong Xu

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments 11 pages, 4 figures, 5 tables

1909.02128 2019-11-21 cs.AI cs.LG cs.MA 88%

No Press Diplomacy: Modeling Multi-Agent Gameplay

Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted at NeurIPS 2019

1910.14472 2019-11-01 cs.LG cs.AI cs.MA stat.ML 88%

Learning Fairness in Multi-Agent Systems

Jiechuan Jiang, Zongqing Lu

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments NeurIPS'19

1904.00601 2019-10-23 cs.LG cs.AI cs.IT math.IT stat.ML 88%

Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach

Mohit K. Sharma, Alessio Zappone, Mohamad Assaad, Merouane Debbah, Spyridon Vassilaras

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

1905.01296 2019-10-01 cs.CV cs.AI cs.LG cs.RO stat.ML 88%

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

Nicholas Rhinehart, Rowan McAllister, Kris Kitani, Sergey Levine

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments To appear at the IEEE International Conference on Computer Vision (ICCV 2019). Website: https://sites.google.com/view/precog

1811.01458 2019-09-12 cs.MA cs.AI cs.LG 88%

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

Jakob N. Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

1908.06758 2019-08-20 cs.LG cs.AI 88%

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning

Jiancheng Long, Hongming Zhang, Tianyang Yu, Bo Xu

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

1908.04573 2019-08-19 cs.LG cs.AI cs.MA stat.ML 88%

Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking

Yue Wang, Yao Wan, Chenwei Zhang, Lixin Cui, Lu Bai, Philip S. Yu

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments This paper is accepted by ICDM'19 as a short paper

1403.6508 2019-07-31 cs.GT cs.AI cs.LG 88%

Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games

Xiaomin Lin, Peter A. Beling, Randy Cogill

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Journal ref IEEE Transactions on Games 10(1) (2018) 56-68

1806.04562 2019-07-30 cs.LG cs.AI stat.ML 88%

Multi-Agent Deep Reinforcement Learning with Human Strategies

Thanh Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments 2019 IEEE International Conference on Industrial Technology (ICIT), Melbourne, Australia

Journal ref 2019 IEEE International Conference on Industrial Technology (ICIT)

1810.03679 2019-06-28 cs.LG cs.AI cs.MA stat.ML 88%

Multi-agent Deep Reinforcement Learning for Zero Energy Communities

Amit Prasad, Ivana Dusparic

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted at ISGT Europe 2019

1906.10124 2019-06-26 cs.MA cs.AI cs.HC cs.LG 88%

On Multi-Agent Learning in Team Sports Games

Yunqi Zhao, Igor Borovikov, Jason Rupert, Caedmon Somers, Ahmad Beirami

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments Presented at ICML 2019 Workshop on Imitation, Intent, and Interaction (I3). arXiv admin note: substantial text overlap with arXiv:1903.10545

1810.08647 2019-06-20 cs.LG cs.AI cs.MA stat.ML 88%

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

1906.04737 2019-06-13 cs.LG cs.AI cs.MA stat.ML 88%

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, Stefano V. Albrecht

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

1906.03037 2019-06-10 cs.CY cs.AI cs.LG cs.MA 88%

Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness

Vidyasagar Sadhu, Gabriel Salles-Loustau, Dario Pompili, Saman Zonouz, Vincent Sritapan

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Journal ref 2016 IEEE International Conference on Autonomic Computing (ICAC), Wurzburg, 2016, pp. 251-256

1903.04959 2019-06-04 cs.LG cs.AI cs.MA stat.ML 88%

Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces

Haotian Fu, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, Changjie Fan

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Journal ref IJCAI 2019

1810.02912 2019-05-29 cs.LG cs.AI cs.MA stat.ML 88%

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

Shariq Iqbal, Fei Sha

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments ICML 2019 camera ready version

1903.10654 2019-05-28 cs.LG cs.AI cs.RO 88%

Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving

Akifumi Wachi

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

1905.05408 2019-05-15 cs.LG cs.AI cs.MA stat.ML 88%

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, Yung Yi

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments 18 pages; Accepted to ICML 2019

1809.05096 2019-05-08 cs.MA cs.AI cs.LG 88%

Negative Update Intervals in Deep Multi-Agent Reinforcement Learning

Gregory Palmer, Rahul Savani, Karl Tuyls

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments 11 Pages, 6 Figures, AAMAS2019 Conference Proceedings

1905.01303 2019-05-07 cs.LG cs.AI cs.MA stat.ML 88%

Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach

Marc Brittain, Peng Wei

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments 10 pages

1903.05766 2019-03-15 cs.MA cs.AI cs.LG 88%

Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning

Raunak P. Bhattacharyya, Derek J. Phillips, Changliu Liu, Jayesh K. Gupta, Katherine Driggs-Campbell, Mykel J. Kochenderfer

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted for publication at ICRA 2019

1810.00147 2019-03-08 cs.AI cs.LG cs.MA stat.ML 88%

M$^3$RL: Mind-aware Multi-agent Management Reinforcement Learning

Tianmin Shu, Yuandong Tian

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments ICLR 2019; 18 pages, 12 figures

1903.01365 2019-03-05 cs.MA cs.AI cs.LG 88%

Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning

Giulio Bacchiani, Daniele Molinari, Marco Patander

专题命中多智能体：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

1. 多智能体 14943 篇

Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping

Multi-Agent Common Knowledge Reinforcement Learning

Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances

BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)

Multi-Agent Deep Reinforcement Learning with Adaptive Policies

Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

MANELA: A Multi-Agent Algorithm for Learning Network Embeddings

No Press Diplomacy: Modeling Multi-Agent Gameplay

Learning Fairness in Multi-Agent Systems

Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning

Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking

Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games

Multi-Agent Deep Reinforcement Learning with Human Strategies

Multi-agent Deep Reinforcement Learning for Zero Energy Communities

On Multi-Agent Learning in Team Sports Games

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness

Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

Negative Update Intervals in Deep Multi-Agent Reinforcement Learning

Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach

Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning

M$^3$RL: Mind-aware Multi-agent Management Reinforcement Learning

Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning