Conor F. Hayes

Orcid: 0000-0003-4783-7126

According to our database1, Conor F. Hayes authored at least 14 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2024
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning.
CoRR, 2024

2023
Actor-critic multi-objective reinforcement learning for non-linear utility functions.
Auton. Agents Multi Agent Syst., October, 2023

Monte Carlo tree search algorithms for risk-aware and multi-objective reinforcement learning.
Auton. Agents Multi Agent Syst., October, 2023

Distributional Multi-Objective Decision Making.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

A Brief Guide to Multi-Objective Reinforcement Learning and Planning.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Scalar Reward is Not Enough.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models.
CoRR, 2022

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning.
CoRR, 2022

Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021).
Auton. Agents Multi Agent Syst., 2022

A practical guide to multi-objective reinforcement learning and planning.
Auton. Agents Multi Agent Syst., 2022

Decision-Theoretic Planning for the Expected Scalarised Returns.
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making.
CoRR, 2021

Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search.
CoRR, 2021

Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning.
Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021


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