A Survey on Reinforcement Learning in Aviation Applications
- URL: http://arxiv.org/abs/2211.02147v1
- Date: Thu, 3 Nov 2022 21:10:25 GMT
- Title: A Survey on Reinforcement Learning in Aviation Applications
- Authors: Pouria Razzaghi and Amin Tabrizian and Wei Guo and Shulu Chen and
Abenezer Taye and Ellis Thompson and Alexis Bregeon and Ali Baheri and Peng
Wei
- Abstract summary: reinforcement learning provides a data-driven, learning-based framework to formulate and solve sequential decision-making problems.
The RL framework has become promising due to largely improved data availability and computing power in the aviation industry.
- Score: 5.7528776426748625
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Compared with model-based control and optimization methods, reinforcement
learning (RL) provides a data-driven, learning-based framework to formulate and
solve sequential decision-making problems. The RL framework has become
promising due to largely improved data availability and computing power in the
aviation industry. Many aviation-based applications can be formulated or
treated as sequential decision-making problems. Some of them are offline
planning problems, while others need to be solved online and are
safety-critical. In this survey paper, we first describe standard RL
formulations and solutions. Then we survey the landscape of existing RL-based
applications in aviation. Finally, we summarize the paper, identify the
technical gaps, and suggest future directions of RL research in aviation.
Related papers
- Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments [24.09560293826079]
Ground Delay Programs (GDP) is a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports.
We developed two RL models: Behavioral Cloning (BC) and Conservative Q-Learning (CQL)
These models are designed to enhance GDP efficiency by utilizing a sophisticated reward function that integrates ground and airborne delays and terminal area congestion.
arXiv Detail & Related papers (2024-05-14T03:48:45Z) - A Survey of Meta-Reinforcement Learning [83.95180398234238]
We cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL.
We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task.
We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.
arXiv Detail & Related papers (2023-01-19T12:01:41Z) - A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open
Problems [0.0]
Reinforcement learning (RL) has experienced a dramatic increase in popularity.
There is still a wide range of domains inaccessible to RL due to the high cost and danger of interacting with the environment.
offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions.
arXiv Detail & Related papers (2022-03-02T20:05:11Z) - Pessimistic Model Selection for Offline Deep Reinforcement Learning [56.282483586473816]
Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications.
One main barrier is the over-fitting issue that leads to poor generalizability of the policy learned by DRL.
We propose a pessimistic model selection (PMS) approach for offline DRL with a theoretical guarantee.
arXiv Detail & Related papers (2021-11-29T06:29:49Z) - Offline Reinforcement Learning from Images with Latent Space Models [60.69745540036375]
offline reinforcement learning (RL) refers to the problem of learning policies from a static dataset of environment interactions.
We build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces.
Our approach is both tractable in practice and corresponds to maximizing a lower bound of the ELBO in the unknown POMDP.
arXiv Detail & Related papers (2020-12-21T18:28:17Z) - Deep Reinforcement Learning and Transportation Research: A Comprehensive
Review [0.0]
We offer an overview of the DRL mathematical background, popular and promising DRL algorithms, and some highly effective DRL extensions.
Building on this review, we examine the applicability, strengths, shortcomings, and common and application-specific issues of DRL techniques with regard to their applications in transportation.
arXiv Detail & Related papers (2020-10-13T05:23:11Z) - Critic Regularized Regression [70.8487887738354]
We propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR)
We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces.
arXiv Detail & Related papers (2020-06-26T17:50:26Z) - RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning [108.9599280270704]
We propose a benchmark called RL Unplugged to evaluate and compare offline RL methods.
RL Unplugged includes data from a diverse range of domains including games and simulated motor control problems.
We will release data for all our tasks and open-source all algorithms presented in this paper.
arXiv Detail & Related papers (2020-06-24T17:14:51Z) - MOPO: Model-based Offline Policy Optimization [183.6449600580806]
offline reinforcement learning (RL) refers to the problem of learning policies entirely from a large batch of previously collected data.
We show that an existing model-based RL algorithm already produces significant gains in the offline setting.
We propose to modify the existing model-based RL methods by applying them with rewards artificially penalized by the uncertainty of the dynamics.
arXiv Detail & Related papers (2020-05-27T08:46:41Z) - A Survey of Reinforcement Learning Algorithms for Dynamically Varying
Environments [1.713291434132985]
Reinforcement learning (RL) algorithms find applications in inventory control, recommender systems, vehicular traffic management, cloud computing and robotics.
Real-world complications of many tasks arising in these domains makes them difficult to solve with the basic assumptions underlying classical RL algorithms.
This paper provides a survey of RL methods developed for handling dynamically varying environment models.
A representative collection of these algorithms is discussed in detail in this work along with their categorization and their relative merits and demerits.
arXiv Detail & Related papers (2020-05-19T09:42:42Z) - Deep Reinforcement Learning for Intelligent Transportation Systems: A
Survey [23.300763504208597]
Combining data-driven applications with transportation systems plays a key role in recent transportation applications.
Deep reinforcement learning (RL) based traffic control applications are surveyed.
arXiv Detail & Related papers (2020-05-02T22:44:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.