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Robert C. Grande

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2 papers

1 author row

ICRA Conference 2014 Conference Paper

Human aware UAS path planning in urban environments using nonstationary MDPs

Rakshit Allamaraju
Hassan A. Kingravi
Allan Axelrod
Girish Chowdhary 0001
Robert C. Grande
Jonathan P. How
Christopher Crick
Weihua Sheng

A growing concern with deploying Unmanned Aerial Vehicles (UAVs) in urban environments is the potential violation of human privacy, and the backlash this could entail. Therefore, there is a need for UAV path planning algorithms that minimize the likelihood of invading human privacy. We formulate the problem of human-aware path planning as a nonstationary Markov Decision Process, and provide a novel model-based reinforcement learning solution that leverages Gaussian process clustering. Our algorithm is flexible enough to accommodate changes in human population densities by employing Bayesian nonparametrics, and is real-time computable. The approach is validated experimentally on a large-scale long duration experiment with both simulated and real UAVs.

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ICML Conference 2014 Conference Paper

Sample Efficient Reinforcement Learning with Gaussian Processes

Robert C. Grande
Thomas J. Walsh 0001
Jonathan P. How

This paper derives sample complexity results for using Gaussian Processes (GPs) in both model-based and model-free reinforcement learning (RL). We show that GPs are KWIK learnable, proving for the first time that a model-based RL approach using GPs, GP-Rmax, is sample efficient (PAC-MDP). However, we then show that previous approaches to model-free RL using GPs take an exponential number of steps to find an optimal policy, and are therefore not sample efficient. The third and main contribution is the introduction of a model-free RL algorithm using GPs, DGPQ, which is sample efficient and, in contrast to model-based algorithms, capable of acting in real time, as demonstrated on a five-dimensional aircraft simulator.

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