Results 51 to 60 of about 5,876,040 (331)
Reinforcement Learning Approaches in Social Robotics
Sensors, 2021 This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior.
Neziha Akalin, Amy Loutfi
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Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties
Frontiers in Robotics and AI, 2021 Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases.
S. M. Nahid Mahmud +3 more
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Reinforcement Learning: Theory and Applications in HEMS
Energies, 2022 The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article.
Omar Al-Ani, Sanjoy Das
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Stochastic Reinforcement Learning
, 2019 In reinforcement learning episodes, the rewards and punishments are often non-deterministic, and there are invariably stochastic elements governing the underlying situation.
Kuang, Nikki Lijing +2 more
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Submodular Reinforcement Learning
Proc. International Conference on Learning Representations (ICLR), 2023 Spotlight paper at ICLR ...
Prajapat, Manish; id_orcid0000-0002-3867-4575 +3 more
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Virtual to Real Reinforcement Learning for Autonomous Driving
, 2017 Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first
Lu, Cewu +3 more
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Parallel model-based and model-free reinforcement learning for card sorting performance
Scientific Reports, 2020 The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility ...
Alexander Steinke +2 more
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Atari games and Intel processors
, 2017 The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes them exceptionally suitable for CPU computations.
Adamski, Robert +3 more
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Selection in Scale-Free Small World
, 2005 In this paper we compare the performance characteristics of our selection based learning algorithm for Web crawlers with the characteristics of the reinforcement learning algorithm. The task of the crawlers is to find new information on the Web.
Farkas, Cs., Lorincz, A., Palotai, Zs.
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Curriculum Learning in Reinforcement Learning [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Transfer learning in reinforcement learning is an area of research that seeks to speed up or improve learning of a complex target task, by leveraging knowledge from one or more source tasks. This thesis will extend the concept of transfer learning to curriculum learning, where the goal is to design a sequence of source tasks for an agent to train on ...
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