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Found 15 papers, 6 papers with code
Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality
Though it is complete and optimal, it does not scale well.
Monte Carlo Tree Descent for Black-Box Optimization
The key to Black-Box Optimization is to efficiently search through input regions with potentially widely-varying numerical properties, to achieve low-regret descent and fast progress toward the optima.
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems
Long-term fairness is an important factor of consideration in designing and deploying learning-based decision systems in high-stake decision-making contexts.
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks
We propose new learning-based methods for reducing collision checking to accelerate motion planning by training graph neural networks (GNNs) that perform path exploration and path smoothing.
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments.
SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge
To address the challenges, we propose Self-Supervised ContrAstive Lifelong LEarning without Prior Knowledge (SCALE) which can extract and memorize representations on-the-fly purely from the data continuum.
Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model
In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user's sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally.
Learning Bounded Context-Free-Grammar via LSTM and the Transformer:Difference and Explanations
With the forced decomposition, we show that the performance upper bounds of LSTM and Transformer in learning CFL are close: both of them can simulate a stack and perform stack operation along with state transitions.
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models.
Transient Stability Assessment of Networked Microgrids Using Neural Lyapunov Methods
Case studies suggest that the proposed method can address networked microgrids with heterogeneous interface dynamics, and in comparison with conventional methods that are based on quadratic Lyapunov functions, can characterize the security regions with much less conservativeness.
Provably Efficient Model-based Policy Adaptation
The high sample complexity of reinforcement learning challenges its use in practice.
Neural Lyapunov Control
We propose new methods for learning control policies and neural network Lyapunov functions for nonlinear control problems, with provable guarantee of stability.
Learning Latent Representations for Inverse Dynamics using Generalized Experiences
Many practical robot locomotion tasks require agents to use control policies that can be parameterized by goals.
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
A human-specified design choice in domain randomization is the form and parameters of the distribution of simulated environments.
REAS: Combining Numerical Optimization with SAT Solving
In this paper, we present ReaS, a technique that combines numerical optimization with SAT solving to synthesize unknowns in a program that involves discrete and floating point computation.
