Journal Article10.1016/J.ROBOT.2020.103568
Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving
Amarildo Likmeta,Alberto Maria Metelli,Andrea Tirinzoni,Riccardo Giol,Marcello Restelli,Danilo Romano +5 more
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TL;DR: This paper designs parametric rule-based controllers, in which interpretable rules can be provided by domain experts and their parameters are learned via RL, and illustrates how to apply parameter-based RL methods (PGPE) to this setting.
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About: This article is published in Robotics and Autonomous Systems. The article was published on 01 Sep 2020. The article focuses on the topics: Rule-based system & Reinforcement learning.
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Citations
Pattern Recognition and Machine Learning
Christopher M. Bishop
- 01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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A Comprehensive Survey on the Application of Deep and Reinforcement Learning Approaches in Autonomous Driving
TL;DR: In this paper , a survey of deep learning and reinforcement learning based approaches for autonomous driving is presented, focusing on scene understanding, motion planning, decision making, vehicle control, social behavior, and communication.
102
Human-Like Decision Making of Artificial Drivers in Intelligent Transportation Systems: An End-to-End Driving Behavior Prediction Approach
TL;DR: A new, fully end-to-end decision-making method, namely the pyramid pooling convolutional neural network with long short-time memory (PPC-LSTM), for multitask (longitudinal and lateral) decision inference in future ITSs.
39
Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities
Charles Retzlaff,Srijita Das,Christabel Wayllace,Payam Mousavi,Mohammad Afshari,Tianpei Yang,Anna Puspa Amarta Saranti,Alessa Angerschmid,Matthew E. Taylor,Andreas Holzinger +9 more
TL;DR: It is shown how the application of explainable AI (xAI) and specific improvements to existing explainability approaches can enable a better human-agent interaction in HITL RL for all types of users, whether for lay people, domain experts, or machine learning specialists.
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A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms
Andrei Aksjonov,Ville Kyrki +1 more
TL;DR: This paper proposes a decision making and control framework, which profits from advantages of both the ruleand machine-learning-based techniques while compensating for their disadvantages.
References
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
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.
138.5K
•Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
82.5K
•Book
Reinforcement Learning: An Introduction
Richard S. Sutton,Andrew G. Barto +1 more
- 01 Jan 1988
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.
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.
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
30.8K