Deep Reinforcement Learning for Autonomous Driving: A Survey
B Ravi Kiran,Ibrahim Sobh,Victor Talpaert,Patrick Mannion,Ahmad A. Al Sallab,Senthil Yogamani,Patrick Pérez +6 more
TL;DR: This review summarises deep reinforcement learning algorithms, provides a taxonomy of automated driving tasks where (D)RL methods have been employed, highlights the key challenges algorithmically as well as in terms of deployment of real world autonomous driving agents, the role of simulators in training agents, and finally methods to evaluate, test and robustifying existing solutions in RL and imitation learning.
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Abstract: With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.
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Beyond Supervised Continual Learning: a Review
TL;DR: Books that study CL in other settings, such as learning with reduced supervision, fully unsupervised learning, and reinforcement learning are reviewed, with a simple schema for classifying CL approaches w.r.t. their level of autonomy and supervision.
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In-Sample Policy Iteration for Offline Reinforcement Learning
09 Jun 2023
TL;DR: In this paper , the authors propose a novel algorithm employing in-sample policy iteration that substantially enhances behavior-regularized methods in offline RL, which gradually improves itself while implicitly avoiding querying out-of-sample actions to avert catastrophic learning failures.
Top-down design of protein nanomaterials with reinforcement learning
Isaac D. Lutz,Shunzhi Wang,Christoffer Norn,Andrew J. Borst,Yan Ting Zhao,Annie M. Dosey,Longxing Cao,Zhe Li,Minkyung Baek,Neil P. King,Hannele Ruohola-Baker,D. Baker +11 more
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Proximal Policy Optimization Algorithms
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