1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Preference Inference from Demonstration in Multi-objective Multi-agent Decision Making
Junlin Lu
It is challenging to quantify numerical preferences for different objectives in a multi-objective decision-making problem. However, the demonstrations of a user are often accessibl…
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach
Junlin Lu, Patrick Mannion, Karl Mason
Many decision-making problems feature multiple objectives. In such problems, it is not always possible to know the preferences of a decision-maker for different objectives. However…