Influence-based Decision-making in Uncertain Environments

Influence-Augmented Online Planning for Complex Environments

Autori: He, Jinke; Suau, Miguel; Oliehoek, Frans A.
Pubblicato in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), Numero 33, 2020
Editore: Curran Associates, Inc.

Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems

Autori: Suau, Miguel; He, Jinke; Spaan, Matthijs T. J.; Oliehoek, Frans A.
Pubblicato in: Proceedings of the 39th International Conference on Machine Learning, Numero PMLR 162, 2022, Pagina/e 20604-20624
Editore: PMLR

A Cross-Field Review of State Abstraction for Markov Decision Processes.

Autori: Elena Congeduti, Frans A. Oliehoek
Pubblicato in: In Proceedings of the 34th Benelux Conference on Artificial Intelligence (BNAIC) and the 30th Belgian Dutch Conference on Machine Learning (Benelearn), 2022
Editore: BNAIC/Benelearn

Exploring the effects of conditioning Independent Q-Learners on the Sufficient Statistic for Dec-POMDPs

Autori: Alex Mandersloot, Frans A. Oliehoek, Aleksander Czechowski
Pubblicato in: Proceedings of the 32nd Benelux Conference on Artificial Intelligence (BNAIC) and the 29th Belgian Dutch Conference on Machine Learning (Benelearn), 2020
Editore: BNAIC/Benelearn

Constraint Propagation and Reverse Multi-Agent Learning

Autori: Aleksander Czechowski
Pubblicato in: AAAI Spring Symposium on Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL), 2021
Editore: AAAI

Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs

Autori: Celikok, Mustafa Mert; Frans A. Oliehoek; Samuel Kaski.
Pubblicato in: Proceedings of the Twenty-First International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022, Pagina/e 235-243
Editore: IFAAMAS

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

Autori: Van der Pol, Elise; Daniel E. Worrall; Herke Van Hoof; Frans A. Oliehoek; Max Welling.
Pubblicato in: Advances in Neural Information Processing Systems 33, 2020, Pagina/e 4199–4210
Editore: Curran Associates, Inc.

Difference Rewards Policy Gradients

Autori: Jacopo Castellini; Sam Devlin; Frans A. Oliehoek; Rahul Savani
Pubblicato in: Proceedings of the Twentieth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021, Pagina/e 1475–1477
Editore: IFAAMAS

Influence-aware Memory Architectures for Deep Reinforcement Learning

Autori: Suau, Miguel; He, Jinke; Congeduti, Elena; Starre, Rolf A. N.; Czechowski, Aleksander; Oliehoek, Frans A.
Pubblicato in: NeurIPS'20 Workshop on Deep Reinforcement Learning, 2020
Editore: arxiv

Overcoming Traffic Sensors Malfunctions with Deep Learning

Autori: Victoria Catalan Pastor, Elena Congeduti, Aleksander Czechowski, Frans A. Oliehoek
Pubblicato in: Proceedings of the 34th Benelux Conference on Artificial Intelligence (BNAIC) and the 30th Belgian Dutch Conference on Machine Learning (Benelearn), 2022
Editore: BNVKI

Using Bisimulation Metrics to Analyze and Evaluate Latent State Representations

Autori: Nele Albers, Miguel Suau, Frans A. Oliehoek
Pubblicato in: Proceedings of the 33rd Benelux Conference on Artificial Intelligence (BNAIC) and the 29th Belgian Dutch Conference on Machine Learning (Benelearn), 2021
Editore: BNAIC/Benelearn

Analog Circuit Design with Dyna-Style Reinforcement Learning

Autori: Wook Lee; Frans Oliehoek
Pubblicato in: NeurIPS 2020 Workshop: Machine Learning for Engineering Modeling, Simulation, and Design, 2020
Editore: Online workshop proceedings
DOI: 10.48550/arxiv.2011.07665

Learning Complex Policy Distribution with CEM Guided Adversarial Hypernetwork

Autori: Shi Yuan Tang; Athirai A. Irissappane; Frans A. Oliehoek; Jie Zhang
Pubblicato in: Proceedings of the Twentieth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021, Pagina/e 1308–1316
Editore: IFAAMAS

Speeding up Deep Reinforcement Learning through Influence-Augmented Local Simulators

Autori: Suau, Miguel; He, Jinke; Spaan, Matthijs T. J.; Oliehoek, Frans
Pubblicato in: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022, Pagina/e 1735–1737
Editore: IFAAMAS

BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs

Autori: Katt, Sammie; Hai Nguyen; Frans A. Oliehoek; Christopher Amato.
Pubblicato in: Proceedings of the Twenty-First International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022, ISBN 978-1-4503-9213-6
Editore: IFAAMAS

Poincaré-Bendixson Limit Sets in Multi-Agent Learning

Autori: Czechowski, Aleksander; Piliouras, Georgios
Pubblicato in: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, 2022, Pagina/e 318-326, ISBN 9781450392136
Editore: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity?

Autori: Mey, Alexander; Oliehoek, Frans A
Pubblicato in: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '21), 2021, Pagina/e 23–27, ISBN 978-1-4503-8307-3
Editore: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

Beyond Local Nash Equilibria for Adversarial Networks

Autori: Oliehoek, Frans A; Savani, Rahul; Gallego, Jose; Pol, Elise van der; Groß, Roderich
Pubblicato in: Benelearn 2018 Pre-proceedings, 2018
Editore: BNAIC

Bayesian Reinforcement Learning in Factored POMDPs

Autori: Katt, Sammie; Oliehoek, Frans; Amato, Christopher
Pubblicato in: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019
Editore: International Foundation for Autonomous Agents and Multiagent Systems

The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning

Autori: Castellini, Jacopo; Oliehoek, Frans A.; Savani, Rahul; Whiteson, Shimon
Pubblicato in: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019, Pagina/e 1862-1864, ISBN 978-1-4503-6309-9
Editore: International Foundation for Autonomous Agents and Multiagent Systems

Interactive Learning and Decision Making: Foundations, Insights & Challenges

Autori: Frans A. Oliehoek
Pubblicato in: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018, Pagina/e 5703-5708, ISBN 9780-999241127
Editore: International Joint Conferences on Artificial Intelligence Organization
DOI: 10.24963/ijcai.2018/813

Plannable Approximations to MDP Homomorphisms: Equivariance under Actions

Autori: van der Pol, Elise; Kipf, Thomas; Oliehoek, Frans A.; Welling, Max
Pubblicato in: Proceedings of the Nineteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2020, Pagina/e 1431--1439, ISBN 9781450375184
Editore: International Foundation for Autonomous Agents and Multiagent Systems

Bayesian Reinforcement Learning in Factored POMDPs

Autori: Katt, Sammie; Oliehoek, Frans; Amato, Christopher
Pubblicato in: Proceedings of the Eighteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2019, Pagina/e 7-15, ISBN 978-1-4503-6309-9
Editore: International Foundation for Autonomous Agents and Multiagent Systems

Decentralized MCTS via Learned Teammate Models

Autori: Aleksander Czechowski, Frans A. Oliehoek
Pubblicato in: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, Pagina/e 81-88, ISBN 978-0-9992411-6-5
Editore: International Joint Conferences on Artificial Intelligence Organization
DOI: 10.24963/ijcai.2020/12

Maximizing Information Gain in Partially Observable Environments via Prediction Reward

Autori: Satsangi, Yash; Lim, Sungsu; Whiteson, Shimon; Oliehoek, Frans; White, Martha
Pubblicato in: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 2020, Pagina/e 1215--1223, ISBN 9781450375184
Editore: International Foundation for Autonomous Agents and Multiagent Systems

Influence-Based Abstraction in Deep Reinforcement Learning

Autori: Miguel Suau de Castro, Elena Congeduti, Rolf Starre, Aleksander Czechowski, Frans Oliehoek
Pubblicato in: AAMAS Workshop on Adaptive Learning Agents (ALA), 2019
Editore: https://ala2019.vub.ac.be/

Model-Based Reinforcement Learning with State Abstraction: A Survey.

Autori: Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek.
Pubblicato in: In Proceedings of the 34th Benelux Conference on Artificial Intelligence (BNAIC) and the 30th Belgian Dutch Conference on Machine Learning (Benelearn), 2022
Editore: BNAIC/Benelearn

Alternating Maximization with Behavioral Cloning

Autori: Czechowski, Aleksander; Oliehoek, Frans A.
Pubblicato in: BNAIC/BeneLearn 2020, 2020
Editore: BNAIC/BeneLearn 2020

Multi Robot Surveillance and Planning in Limited Communication Environments

Autori: Vibhav Kedege, Aleksander Czechowski, Ludo Stellingwerff, Frans A. Oliehoek
Pubblicato in: Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, 2022, ISBN 978-989-758-547-0
Editore: SciTePress

Loss Bounds for Approximate Influence-Based Abstraction

Autori: Congeduti, E.; Alexander Mey; Frans Oliehoek
Pubblicato in: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, 2021, Pagina/e 377-385, ISBN 9781450383073
Editore: International Foundation for Autonomous Agents and Multiagent Systems
DOI: 10.48550/arxiv.2011.01788

Online Planning in POMDPs with Self-Improving Simulators

Autori: He, Jinke; Suau de Castro, Miguel; Baier, Hendrik; Kaisers, Michael; Oliehoek, F.A.
Pubblicato in: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI-22}, 2022, Pagina/e 4628--4634}
Editore: International Joint Conferences on Artificial Intelligence Organization
DOI: 10.24963/ijcai.2022/642

Multi-agent active perception with prediction rewards

Autori: Lauri, Mikko; Oliehoek, Frans A.
Pubblicato in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), Numero 33, 2020
Editore: Curran Associates, Inc.

Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems

Autori: Miguel Suau, Jinke He, Mustafa Mert Çelikok, Matthijs T. J. Spaan, Frans A. Oliehoek
Pubblicato in: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022
Editore: NeurIPS

Comparing Exploration Approaches in Deep Reinforcement Learning for Traffic Light Control.

Autori: Yaniv Oren, Rolf A. N., Starre, and Frans A. Oliehoek.
Pubblicato in: Proceedings of the 32nd Benelux Conference on Artificial Intelligence (BNAIC) and the 29th Belgian Dutch Conference on Machine Learning (Benelearn), 2020
Editore: BNAIC/Benelearn

Analog Circuit Design with Dyna-Style Reinforcement Learning

Autori: Lee, Wook; Oliehoek, Frans A.
Pubblicato in: NeurIPS 2020 Workshop: Machine Learning for Engineering Modeling, Simulation, and Design, Numero 1, 2020
Editore: NeurIPS 2020 Workshop: Machine Learning for Engineering Modeling, Simulation, and Design

Multi-Agent MDP Homomorphic Networks

Autori: Van der Pol, Elise; Herke Van Hoof; Frans A. Oliehoek; Max Welling
Pubblicato in: International Conference on Learning Representations, 2022
Editore: openreview.net

An Analysis of Abstracted Model-Based Reinforcement Learning.

Autori: Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek
Pubblicato in: arXiv e-prints, Numero arXiv:2208.14407, 2022
Editore: arXiv

The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning

Autori: Castellini, Jacopo; Oliehoek, F.A.; Savani, Rahul; Whiteson, Shimon
Pubblicato in: arXiv e-prints, 2019
Editore: arxiv.org

A Sufficient Statistic for Influence in Structured Multiagent Environments

Autori: Oliehoek, Frans A.; Witwicki, Stefan; Kaelbling, Leslie P.
Pubblicato in: arXiv e-prints, 2019
Editore: arxiv.org

Difference rewards policy gradients

Autori: Jacopo Castellini; Frans Oliehoek; Sam Devlin; Rahul Savani
Pubblicato in: Neural Computing and Applications, 2022, ISSN 0941-0643
Editore: Springer Verlag
DOI: 10.48550/arxiv.2012.11258

Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning

Autori: Jacopo Castellini; Frans A. Oliehoek; Rahul Savani; Shimon Whiteson
Pubblicato in: Autonomous Agents and Multi-Agent Systems, Numero 35, 2021, ISSN 1387-2532
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10458-021-09506-w

A Sufficient Statistic for Influence in Structured Multiagent Environments

Autori: Frans A. Oliehoek; Stefan J. Witwicki; Leslie Pack Kaelbling
Pubblicato in: Journal of Artificial Intelligence Research, Numero 70, 2021, Pagina/e 789-870, ISSN 1076-9757
Editore: Morgan Kaufmann Publishers, Inc.
DOI: 10.48550/arxiv.1907.09278

General-Sum Multi-Agent Continuous Inverse Optimal Control

Autori: Christian Neumeyer; Frans A. Oliehoek; Dariu M. Gavrila
Pubblicato in: IEEE Robotics and Automation Letters, Numero 6, 2021, Pagina/e 3429 - 3436, ISSN 2377-3766
Editore: IEEE
DOI: 10.1109/lra.2021.3060411

Influence-aware memory architectures for deep reinforcement learning in POMDPs

Autori: Suau, Miguel; He, Jinke; Elena Congeduti; Rolf A.N. Starre; Aleksander Czechowski; Frans A. Oliehoek
Pubblicato in: Neural Computing and Applications, 2022, ISSN 0941-0643
Editore: Springer Verlag
DOI: 10.1007/s00521-022-07691-7

Beyond Local Nash Equilibria for Adversarial Networks

Autori: Frans A. Oliehoek, Rahul Savani, Jose Gallego, Elise van der Pol, Roderich Groß
Pubblicato in: Artificial Intelligence - 30th Benelux Conference, BNAIC 2018, ‘s-Hertogenbosch, The Netherlands, November 8–9, 2018, Revised Selected Papers, Numero 1021, 2019, Pagina/e 73-89, ISBN 978-3-030-31977-9
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-31978-6_7

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