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Patrick Mannion
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2020 – today
- 2024
- [j16]Mathieu Reymond
, Conor F. Hayes
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. Expert Syst. Appl. 249: 123686 (2024) - [c21]Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Willem Röpke, Diederik M. Roijers:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. AAMAS 2024: 2717-2721 - [c20]Nicole Orzan
Learning in Multi-Objective Public Goods Games with Non-Linear Utilities. ECAI 2024: 2749-2756 - [c19]Junlin Lu
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Energy Management. ECAI 2024: 2814-2821 - [i26]Junlin Lu, Patrick Mannion, Karl Mason:
Go-Explore for Residential Energy Management. CoRR abs/2401.07710 (2024) - [i25]Junlin Lu, Patrick Mannion, Karl Mason:
Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management. CoRR abs/2401.07722 (2024) - [i24]Peter Vamplew, Cameron Foale
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. CoRR abs/2402.02665 (2024) - [i23]Willem Röpke, Mathieu Reymond, Patrick Mannion, Diederik M. Roijers, Ann Nowé, Roxana Radulescu:
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning. CoRR abs/2402.07182 (2024) - [i22]Junlin Lu, Patrick Mannion, Karl Mason:
Demonstration Guided Multi-Objective Reinforcement Learning. CoRR abs/2404.03997 (2024) - [i21]Junlin Lu, Patrick Mannion, Karl Mason:
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments. CoRR abs/2407.11489 (2024) - [i20]Florian Felten, Umut Ucak, Hicham Azmani, Gao Peng, Willem Röpke, Hendrik Baier, Patrick Mannion, Diederik M. Roijers, Jordan K. Terry, El-Ghazali Talbi, Grégoire Danoy, Ann Nowé, Roxana Radulescu:
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning. CoRR abs/2407.16312 (2024) - [i19]Nicole Orzan, Erman Acar, Davide Grossi, Patrick Mannion, Roxana Radulescu:
Learning in Multi-Objective Public Goods Games with Non-Linear Utilities. CoRR abs/2408.00682 (2024) - [i18]Junlin Lu, Patrick Mannion, Karl Mason:
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning. CoRR abs/2409.20258 (2024) - 2023
- [j15]Conor F. Hayes
Monte Carlo tree search algorithms for risk-aware and multi-objective reinforcement learning. Auton. Agents Multi Agent Syst. 37(2): 26 (2023) - [j14]Karl Mason
Special issue on evolutionary machine learning. Knowl. Eng. Rev. 38 (2023) - [c18]Seán Caulfield Curley, Karl Mason, Patrick Mannion:
Know Your Enemy: Identifying Adversarial Behaviours in Deep Reinforcement Learning Agents (Student Abstract). AAAI 2023: 16176-16177 - [c17]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Friedrik Hentz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar Reward is Not Enough. AAMAS 2023: 839-841 - [c16]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Brief Guide to Multi-Objective Reinforcement Learning and Planning. AAMAS 2023: 1988-1990 - [c15]Seán Caulfield Curley, Karl Mason, Patrick Mannion:
Know Your Enemy: Identifying and Adapting to Adversarial Attacks in Deep Reinforcement Learning. AAMAS 2023: 2813-2814 - [c14]Junlin Lu, Patrick Mannion, Karl Mason:
Go-Explore for Residential Energy Management. ECAI Workshops (2) 2023: 133-139 - [c13]Willem Röpke
Distributional Multi-Objective Decision Making. IJCAI 2023: 5711-5719 - [i17]Junlin Lu, Patrick Mannion, Karl Mason:
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach. CoRR abs/2304.14115 (2023) - [i16]Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowé, Diederik M. Roijers:
Distributional Multi-Objective Decision Making. CoRR abs/2305.05560 (2023) - [i15]Adam Callaghan, Karl Mason, Patrick Mannion:
Evolutionary Strategy Guided Reinforcement Learning via MultiBuffer Communication. CoRR abs/2306.11535 (2023) - 2022
- [j13]Conor F. Hayes
A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst. 36(1): 26 (2022) - [j12]Peter Vamplew
Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021). Auton. Agents Multi Agent Syst. 36(2): 41 (2022) - [j11]Felipe Leno da Silva, Patrick MacAlpine, Roxana Radulescu
Special issue on adaptive and learning agents 2020. Neural Comput. Appl. 34(3): 1649-1651 (2022) - [j10]Roxana Radulescu
Opponent learning awareness and modelling in multi-objective normal form games. Neural Comput. Appl. 34(3): 1759-1781 (2022) - [j9]B. Ravi Kiran
Deep Reinforcement Learning for Autonomous Driving: A Survey. IEEE Trans. Intell. Transp. Syst. 23(6): 4909-4926 (2022) - [c12]Conor F. Hayes, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Decision-Theoretic Planning for the Expected Scalarised Returns. AAMAS 2022: 1621-1623 - [i14]Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Radulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin:
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. CoRR abs/2204.05027 (2022) - [i13]Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models. CoRR abs/2207.00368 (2022) - [i12]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning. CoRR abs/2211.13032 (2022) - 2021
- [j8]Patrick Mannion
Special issue on adaptive and learning agents 2018. Knowl. Eng. Rev. 36: e7 (2021) - [c11]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning. AAMAS 2021: 1530-1532 - [c10]David O'Callaghan, Patrick Mannion:
Tunable Behaviours in Sequential Social Dilemmas using Multi-Objective Reinforcement Learning. AAMAS 2021: 1610-1612 - [i11]David O'Callaghan, Patrick Mannion:
Exploring the Impact of Tunable Agents in Sequential Social Dilemmas. CoRR abs/2101.11967 (2021) - [i10]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search. CoRR abs/2102.00966 (2021) - [i9]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Practical Guide to Multi-Objective Reinforcement Learning and Planning. CoRR abs/2103.09568 (2021) - [i8]Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making. CoRR abs/2106.01048 (2021) - [i7]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Fredrik Heintz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021). CoRR abs/2112.15422 (2021) - 2020
- [j7]Roxana Radulescu
Multi-objective multi-agent decision making: a utility-based analysis and survey. Auton. Agents Multi Agent Syst. 34(1): 10 (2020) - [j6]Patrick Mannion
Special issue on adaptive and learning agents 2019. Knowl. Eng. Rev. 35: e18 (2020) - [j5]Roxana Radulescu
A utility-based analysis of equilibria in multi-objective normal-form games. Knowl. Eng. Rev. 35: e32 (2020) - [c9]Yijie Zhang, Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Opponent Modelling for Reinforcement Learning in Multi-Objective Normal Form Games. AAMAS 2020: 2080-2082 - [c8]Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey. AAMAS 2020: 2158-2160 - [i6]Roxana Radulescu, Patrick Mannion, Yijie Zhang, Diederik M. Roijers, Ann Nowé:
A utility-based analysis of equilibria in multi-objective normal form games. CoRR abs/2001.08177 (2020) - [i5]Bangalore Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A. Al Sallab, Senthil Kumar Yogamani, Patrick Pérez:
Deep Reinforcement Learning for Autonomous Driving: A Survey. CoRR abs/2002.00444 (2020) - [i4]Roxana Radulescu, Timothy Verstraeten, Yijie Zhang, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games. CoRR abs/2011.07290 (2020)
2010 – 2019
- 2019
- [c7]Golden Rockefeller, Patrick Mannion, Kagan Tumer:
Curriculum Learning for Tightly Coupled Multiagent Systems. AAMAS 2019: 2174-2176 - [c6]Golden Rockefeller, Patrick Mannion, Kagan Tumer:
Fitness Critics for Multiagent Learning: Extended Abstract. MRS 2019: 222-224 - [c5]Victor Talpaert, Ibrahim Sobh, B. Ravi Kiran, Patrick Mannion, Senthil Kumar Yogamani, Ahmad El Sallab, Patrick Pérez:
Exploring Applications of Deep Reinforcement Learning for Real-world Autonomous Driving Systems. VISIGRAPP (5: VISAPP) 2019: 564-572 - [i3]Victor Talpaert, Ibrahim Sobh, B. Ravi Kiran, Patrick Mannion, Senthil Kumar Yogamani, Ahmad El Sallab, Patrick Pérez:
Exploring applications of deep reinforcement learning for real-world autonomous driving systems. CoRR abs/1901.01536 (2019) - [i2]Patrick Mannion:
Vulnerable road user detection: state-of-the-art and open challenges. CoRR abs/1902.03601 (2019) - [i1]Roxana Radulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé:
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey. CoRR abs/1909.02964 (2019) - 2018
- [j4]Patrick Mannion
Special issue on adaptive and learning agents 2017. Knowl. Eng. Rev. 33: e18 (2018) - [j3]Patrick Mannion
Reward shaping for knowledge-based multi-objective multi-agent reinforcement learning. Knowl. Eng. Rev. 33: e23 (2018) - 2017
- [j2]Patrick Mannion
Policy invariance under reward transformations for multi-objective reinforcement learning. Neurocomputing 263: 60-73 (2017) - [j1]Patrick Mannion
Multi-agent credit assignment in stochastic resource management games. Knowl. Eng. Rev. 32: e16 (2017) - [c4]Patrick Mannion, Jim Duggan, Enda Howley:
A Theoretical and Empirical Analysis of Reward Transformations in Multi-Objective Stochastic Games. AAMAS 2017: 1625-1627 - 2016
- [c3]Patrick Mannion, Karl Mason, Sam Devlin, Jim Duggan, Enda Howley:
Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning: (Extended Abstract). AAMAS 2016: 1345-1346 - [p1]Patrick Mannion, Jim Duggan, Enda Howley:
An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control. Autonomic Road Transport Support Systems 2016: 47-66 - 2015
- [c2]Patrick Mannion
Parallel Reinforcement Learning for Traffic Signal Control. ANT/SEIT 2015: 956-961
2000 – 2009
- 2006
- [c1]Patrick Mannion:
Integrating UWB in consumer devices. CCNC 2006: 1281
Coauthor Index
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last updated on 2024-12-10 20:45 CET by the dblp team
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