Learning to Navigate Through Complex Dynamic Environment With Modular Deep Reinforcement Learning
Yuanda Wang,Haibo He,Changyin Sun +2 more
- Vol. 10, Iss: 4, pp 400-412
TLDR
The proposed end-to-end modular reinforcement learning architecture for a navigation task in complex dynamic environments with rapidly moving obstacles can efficiently avoid moving obstacles and complete the navigation task at a high success rate.Citations
More filters
Journal ArticleDOI
A Survey of Machine Learning for Indoor Positioning
TL;DR: A comprehensive survey of ML enabled localization techniques using most common wireless technologies for accurate indoor positioning and how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS is provided.
Journal ArticleDOI
Deep reinforcement learning based mobile robot navigation: A review
TL;DR: This paper systematically compares and analyzes the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation; and describes the development of DRL-based navigation.
Journal ArticleDOI
Deterministic Policy Gradient With Integral Compensator for Robust Quadrotor Control
TL;DR: A deep reinforcement learning-based robust control strategy for quadrotor helicopters which introduces an integral compensator to the actor-critic structure and shows that the online learning could significantly improve the control performance.
Journal ArticleDOI
End-to-End Navigation Strategy With Deep Reinforcement Learning for Mobile Robots
TL;DR: An end-to-end navigation planner that translates sparse laser ranging results into movement actions and achieves map-less navigation in complex environments through a reward signal that is enhanced by intrinsic motivation, the agent explores more efficiently, and the learned strategy is more reliable.
Journal ArticleDOI
Cooperative control for multi-player pursuit-evasion games with reinforcement learning
Yuanda Wang,Lu Dong,Changyin Sun +2 more
TL;DR: The training and evaluation results demonstrate that the pursuit team could learn highly efficient cooperative control and communication policies and can capture a superior evader driven by an intelligent escape policy with a high success rate.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Related Papers (5)
Human-level control through deep reinforcement learning
Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation
Lei Tai,Giuseppe Paolo,Ming Liu +2 more