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20242026
most citedInferring Preferences from Demonstrations in Multi-objective Reinforcement Learning

2 citations · 3 across the 8 of their papers we have counts for

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cs.AI2024★ 2 cited

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…

cs.LG2024

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…

cs.LG2024

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…

cs.AI2024★ 1 cited

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…

cs.AI2024

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…