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Found 12 papers, 5 papers with code
Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics
To this end, we present $\textit{PackeRL}$, the first packet-level Reinforcement Learning environment for routing in generic network topologies.
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
We propose KalMamba, an efficient architecture to learn representations for RL that combines the strengths of probabilistic SSMs with the scalability of deterministic SSMs.
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations
To balance computational speed and accuracy meshes with adaptive resolution are used, allocating more resources to critical parts of the geometry.
Adaptive Swarm Mesh Refinement using Deep Reinforcement Learning with Local Rewards
Adaptive Mesh Refinement (AMR) improves the FEM by dynamically allocating mesh elements on the domain, balancing computational speed and accuracy.
Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
This work introduces PI-MGNs, a hybrid approach that combines PINNs and MGNs to quickly and accurately solve non-stationary and nonlinear partial differential equations (PDEs) on arbitrary meshes.
Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions.
Latent Task-Specific Graph Network Simulators
Movement primitives further allow us to accommodate various types of context data, as demonstrated through the utilization of point clouds during inference.
Swarm Reinforcement Learning For Adaptive Mesh Refinement
Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enabling a favorable trade-off between computational speed and simulation accuracy.
Grounding Graph Network Simulators using Physical Sensor Observations
Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail.
Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors
We find that the geometric descriptors greatly help in generalizing to new task configurations and that combining them with our distribution-matching objective is crucial for representing and reproducing versatile behavior.
Versatile Inverse Reinforcement Learning via Cumulative Rewards
Inverse Reinforcement Learning infers a reward function from expert demonstrations, aiming to encode the behavior and intentions of the expert.
Bimodal Speech Emotion Recognition Using Pre-Trained Language Models
Speech emotion recognition is a challenging task and an important step towards more natural human-machine interaction.
