1 citations · 5 across the 8 of their papers we have counts for
8 papers
Decomposing Control Lyapunov Functions for Efficient Reinforcement Learning
Antonio Lopez, David Fridovich-Keil
Recent methods using Reinforcement Learning (RL) have proven to be successful for training intelligent agents in unknown environments. However, RL has not been applied widely in re…
Learning Hyperplanes for Multi-Agent Collision Avoidance in Space
Fernando Palafox, Yue Yu, David Fridovich-Keil
A core challenge of multi-robot interactions is collision avoidance among robots with potentially conflicting objectives. We propose a game-theoretic method for collision avoidance…
Connected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructure
Jiankai Sun, Shreyas Kousik, David Fridovich-Keil +1
Connected autonomous vehicles (CAVs) promise to enhance safety, efficiency, and sustainability in urban transportation. However, this is contingent upon a CAV correctly predicting…
Risk-Minimizing Two-Player Zero-Sum Stochastic Differential Game via Path Integral Control
Apurva Patil, Yujing Zhou, David Fridovich-Keil +1
This paper addresses a continuous-time risk-minimizing two-player zero-sum stochastic differential game (SDG), in which each player aims to minimize its probability of failure. Fai…
Online and Offline Learning of Player Objectives from Partial Observations in Dynamic Games
Lasse Peters, Vicenç Rubies-Royo, Claire J. Tomlin +4
Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scena…
GrAVITree: Graph-based Approximate Value Function In a Tree
Patrick H. Washington, David Fridovich-Keil, Mac Schwager
In this paper, we introduce GrAVITree, a tree- and sampling-based algorithm to compute a near-optimal value function and corresponding feedback policy for indefinite time-horizon,…