7 citations · 7 across the 6 of their papers we have counts for
6 papers
Reinforcement Learning for Sequential Solar PV Policy Design under Uncertainty: An Agent-Based Approach
Iias Faiud, Jonaid Shianifar, Michael Schukat +1
Designing effective and fiscally sustainable policies for solar photovoltaic (PV) adoption requires balancing adoption gains against public expenditure under uncertainty and hetero…
LLM-Assisted Behavioural and Scenario Augmentation for Agent-Based Energy Adoption Models
Iias Faiud, Hossein Khaleghy, Michael Schukat +1
Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting structured behavioural assumption…
Hindsight Preference Replay Improves Preference-Conditioned Multi-Objective Reinforcement Learning
Jonaid Shianifar, Michael Schukat, Karl Mason
Multi-objective reinforcement learning (MORL) enables agents to optimize vector-valued rewards while respecting user preferences. CAPQL, a preference-conditioned actor-critic metho…
Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control
Jonaid Shianifar, Michael Schukat, Karl Mason
In this paper, we explore the optimization of hyperparameters for the Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms using the Tree-structured Parzen Est…
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…
Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-based Approach
A. Wahid, I faiud, K. Mason
This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a…