Learning to soar: Resource-constrained exploration in reinforcement learning

By Jen Jen Chung, Nicholas R. J. Lawrance, Salah Sukkarieh. 2015

42 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Lei Tai

In Artificial Intelligence

1. Emmanuel Rachelson
2. Erwan Lecarpentier
3. Lei Tai

In Computational Theory and Mathematics

1. Emmanuel Rachelson
2. Erwan Lecarpentier
3. Lei Tai

Citations in the corpus

• Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, background): “…are solvable by reinforcement learning, such as inverted helicopter flight learning (Kim et al., 2003; Ng et al., 2006), gliding aircraft control (Chung et al., 2015), mobile robot navigation (Kretzschmar et al., 2016), modular robot control (Varshavskaya et al., 2008) and biped walking…”
• Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning (2019, background): “Many real-world problems can be modelled as an NSMDP, such as, for instance, the problem of path planning for a glider immersed in a nonstationary atmosphere (Chung et al., 2015; Lecarpentier et al., 2017), or that of vehicle routing in dynamic traffic congestion.”