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Found 6 papers, 0 papers with code
Learning to Optimise Climate Sensor Placement using a Transformer
In this paper, we introduce a novel sensor placement approach focused on learning improvement heuristics using deep reinforcement learning (RL) methods.
Deep Metric Tensor Regularized Policy Gradient
Armed with these technical developments, we propose a new policy gradient algorithm that learns to minimize the absolute divergence in the Riemannian manifold as an important regularization mechanism, allowing the Riemannian manifold to smoothen its policy gradient vector field.
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients
Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training data.
Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training
In this paper, we propose a new technique to train an ensemble of base learners based on an innovative multi-step integration method.
Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking
Recently, distributed controller architectures have been quickly gaining popularity in Software-Defined Networking (SDN).
Optimizing Controller Placement for Software-Defined Networks
In comparison to communication delay, existing literature on the CPP assumes that the influence of controller workload distribution on network performance is negligible.
