Results 31 to 40 of about 298,347 (266)
Deep-attack over the deep reinforcement learning
Knowledge-Based Systems, 2022 Accepted to Knowledge-Based ...
Yang Li, Quan Pan, Erik Cambria
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Agronomy, 2022 A series-parallel hybrid banana-harvesting robot was previously developed to pick bananas, with inverse kinematics intractable to an address. This paper investigates a deep reinforcement learning-based inverse kinematics solution to guide the banana ...
Guichao Lin +5 more
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Deep Reinforcement Learning [PDF]
, 2019 Die Interaktion mit der umgebenden Welt kann als eine Grundlage des menschlichen Lernens betrachtet werden [93]. Kinder, welche die notigen motorischen Ablaufe zum Besteigen einer Treppe erlernen, haben dafur keinen direkten Lehrer. Stattdessen erarbeiten sie sich die entsprechenden Bewegungsablaufe durch Fehlschlage und Erfolge, durch das Studieren ...
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Deep Reinforcement Learning [PDF]
, 2020 In the last chapter, we studied the various aspects of the brain-academy architecture of the ML Agents Toolkit and understood certain scripts that are very important for the agent to make a decision according to a policy. In this chapter, we will be looking into the core concepts of deep reinforcement learning (RL) through Python and its interaction ...
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Routing algorithms as tools for integrating social distancing with emergency evacuation
Scientific Reports, 2021 One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe
Yi-Lin Tsai +3 more
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Deep multiagent reinforcement learning: challenges and directions
Artificial Intelligence Review, 2022 AbstractThis paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments.
Annie Wong +3 more
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Body Language Analysis Based on Deep Reinforcement Learning [PDF]
ITM Web of ConferencesDeep learning has become one of the core technologies in current artificial intelligence research and application and has triggered revolutionary breakthroughs in many fields, demonstrating powerful learning ability and creativity.
Lu Boyang
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Deep Ordinal Reinforcement Learning
, 2019 Reinforcement learning usually makes use of numerical rewards, which have nice properties but also come with drawbacks and difficulties. Using rewards on an ordinal scale (ordinal rewards) is an alternative to numerical rewards that has received more ...
C Wirth, CJ Watkins, RS Sutton, V Mnih
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Learn to Steer through Deep Reinforcement Learning [PDF]
Sensors, 2018 It is crucial for robots to autonomously steer in complex environments safely without colliding with any obstacles. Compared to conventional methods, deep reinforcement learning-based methods are able to learn from past experiences automatically and enhance the generalization capability to cope with unseen circumstances. Therefore, we propose an end-to-
Keyu Wu +3 more
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Improvement of Deep Reinforcement Learning Using Curriculum in Game Environment
مجله پژوهش در علوم توانبخشی, 2020 Introduction: Training deep curriculum learning is a kind of smart agent training in which, first the simple acts, and then, the difficult acts are trained to smart agent.
Mohammadreza Mohammadnejad +2 more
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