Search Results for author:
Found 8 papers, 1 papers with code
Real-World Deployment of a Hierarchical Uncertainty-Aware Collaborative Multiagent Planning System
In this paper, we describe the deployment of a planning system that used a hierarchy of planners to execute collaborative multiagent navigation tasks in real-world, unknown environments.
The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances.
Composable Learning with Sparse Kernel Representations
We present a reinforcement learning algorithm for learning sparse non-parametric controllers in a Reproducing Kernel Hilbert Space.
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of {\em reward sparsity}.
Intelligent Autonomous Things on the Battlefield
Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments.
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.
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space
Despite their attractiveness, popular perception is that techniques for nonparametric function approximation do not scale to streaming data due to an intractable growth in the amount of storage they require.
Decentralized Dynamic Discriminative Dictionary Learning
We consider discriminative dictionary learning in a distributed online setting, where a network of agents aims to learn a common set of dictionary elements of a feature space and model parameters while sequentially receiving observations.
