Results 31 to 40 of about 301,921 (265)
Applied Sciences, 2018 To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed.
Linfei Yin +3 more
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Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle
IEEE Access, 2020 Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning or pre-programming tasks.
Qilei Zhang +4 more
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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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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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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 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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Applied Artificial Intelligence, 2023 Nowadays, mobile robots are being widely employed in various settings, including factories, homes, and everyday tasks. Achieving successful implementation of autonomous robot movement largely depends on effective route planning.
Jieren Tan
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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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