Search Results for author:
Found 6 papers, 3 papers with code
Composable Learning with Sparse Kernel Representations
We present a reinforcement learning algorithm for learning sparse non-parametric controllers in a Reproducing Kernel Hilbert Space.
Learning Connectivity for Data Distribution in Robot Teams
To overcome this challenge, we propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN).
Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning
Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information.
Graph Neural Networks for Decentralized Controllers
Dynamical systems comprised of autonomous agents arise in many relevant problems such as multi-agent robotics, smart grids, or smart cities.
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks
We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications.
Robotics
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems
We consider Markov Decision Problems defined over continuous state and action spaces, where an autonomous agent seeks to learn a map from its states to actions so as to maximize its long-term discounted accumulation of rewards.
