Search Results for author:
Found 3 papers, 1 papers with code
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning (MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a platform for benchmarking the scalability of cooperative MARL methods in real-world engineering applications.
Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks
The proposed virtual load monitoring is thoroughly tested through an experimental campaign in an operational offshore wind farm and the results demonstrate the effectiveness of BNN models for fleet-leader-based farm-wide virtual monitoring.
Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach, in which the policies are approximated by actor neural networks guided by a critic network.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
