Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and
engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics …

Autonomous robots operating in uncertain or hazardous environments subject to state safety
constraints must be able to identify and isolate faulty components in a time-optimal manner …

We consider the question of learning $ Q $-function in a sample efficient manner for
reinforcement learning with continuous state and action spaces under a generative model. If …

The ability of a robot to plan complex behaviors with real-time computation, rather than
adhering to predesigned or offline-learned routines, alleviates the need for specialized …

We present a scalable tree search planning algorithm for large multi-agent sequential
decision problems that require dynamic collaboration. Teams of agents need to coordinate …

Partially observable Markov decision processes (POMDPs) provide a flexible representation
for real-world decision and control problems. However, POMDPs are notoriously difficult to …

This work explores on-board planning for the single spacecraft, multiple ground station Earth-
observing satellite scheduling problem through artificial neural network function …

A common technique in reinforcement learning is to evaluate the value function from Monte
Carlo simulations of a given policy, and use the estimated value function to obtain a new …

Large Language Models (LLMs) harness extensive data from the Internet, storing a broad
spectrum of prior knowledge. While LLMs have proven beneficial as decision-making aids …

We present a self-improving, Neural Tree Expansion (NTE) method for multi-robot online
planning in non-cooperative environments, where each robot attempts to maximize its …