5 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…
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…
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…