Results 31 to 40 of about 57,740 (314)
Deep Ordinal Reinforcement Learning [PDF]
, 2020 replaced figures for better visibility, added github repository, more details about source of experimental results, updated target value calculation for standard and ordinal Deep Q ...
Johannes Fürnkranz +2 more
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Scenario-assisted Deep Reinforcement Learning [PDF]
Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development, 2022 Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (
Yerushalmi, Raz +5 more
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Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
Sensors, 2023 Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent
James Orr, Ayan Dutta
doaj +1 more source
Deep Reinforcement Learning: A Brief Survey [PDF]
IEEE Signal Processing Magazine, 2017 Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from ...
Kai Arulkumaran +3 more
openaire +7 more sources
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
doaj +1 more source
Deep Reinforcement Learning: An Overview [PDF]
, 2017 Proceedings of SAI Intelligent Systems Conference (IntelliSys ...
Michael Schukat +2 more
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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
openaire +6 more sources
Deep-attack over the deep reinforcement learning
Knowledge-Based Systems, 2022 Accepted to Knowledge-Based ...
Yang Li, Quan Pan, Erik Cambria
openaire +3 more sources
Deep Reinforcement Learning for Drone Delivery [PDF]
Drones, 2019 Drones are expected to be used extensively for delivery tasks in the future. In the absence of obstacles, satellite based navigation from departure to the geo-located destination is a simple task. When obstacles are known to be in the path, pilots must build a flight plan to avoid them.
Muñoz Ferran, Guillem +3 more
openaire +4 more sources
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
doaj +1 more source