2 citations · 3 across the 7 of their papers we have counts for
7 papers
Demonstration-Guided Continual Reinforcement Learning in Dynamic Environments
Xue Yang, Michael Schukat, Junlin Lu +3
Reinforcement learning (RL) excels in various applications but struggles in dynamic environments where the underlying Markov decision process evolves. Continual reinforcement learn…
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…
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…
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…