Reinforcement Learning for Sequential Solar PV Policy Design under Uncertainty: An Agent-Based Approach
This study formulates PV policy design as a sequential decision problem and integrates reinforcement learning (RL) with a stochastic agent-based model (ABM) that simulates yearly solar PV adoption under uncertainty to demonstrate the potential of RL as a flexible tool for adaptive policy design under uncertainty.
Iias Faiud, Jonaid Shianifar, Michael Schukat et al.
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