2 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning
Junlin Lu, Patrick Mannion, Karl Mason
Many decision-making problems feature multiple objectives where it is not always possible to know the preferences of a human or agent decision-maker for different objectives. Howev…
Learning in Multi-Objective Public Goods Games with Non-Linear Utilities
Nicole Orzan, Erman Acar, Davide Grossi +2
Addressing the question of how to achieve optimal decision-making under risk and uncertainty is crucial for enhancing the capabilities of artificial agents that collaborate with or…
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
Junlin Lu, Patrick Mannion, Karl Mason
Effective residential appliance scheduling is crucial for sustainable living. While multi-objective reinforcement learning (MORL) has proven effective in balancing user preferences…
Demonstration Guided Multi-Objective Reinforcement Learning
Junlin Lu, Patrick Mannion, Karl Mason
Multi-objective reinforcement learning (MORL) is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives. Catering to…
Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
Junlin Lu, Patrick Mannion, Karl Mason
It is often challenging for a user to articulate their preferences accurately in multi-objective decision-making problems. Demonstration-based preference inference (DemoPI) is a pr…
Go-Explore for Residential Energy Management
Junlin Lu, Patrick Mannion, Karl Mason
Reinforcement learning is commonly applied in residential energy management, particularly for optimizing energy costs. However, RL agents often face challenges when dealing with de…