most citedInferring Preferences from Demonstrations in Multi-Objective Residential Energy Management

1 citations · 1 across the 10 of their papers we have counts for

collaborators

10 papers

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

A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization

Nawazish Ali, Rachael Shaw, Karl Mason

Dairy farms consume a significant amount of electricity for their operations, and this research focuses on enhancing energy efficiency and minimizing the impact on the environment…

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.MA2024

Modelling Solar PV Adoption in Irish Dairy Farms using Agent-Based Modelling

Iias Faiud, Michael Schukat, Karl Mason

The agricultural sector is facing mounting demands to enhance energy efficiency within farm enterprises, concurrent with a steady escalation in electricity costs. This paper focuse…

cs.AI20241 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…