Results 211 to 217 of about 476,244 (217)

Learning to Fly with Deep Reinforcement Learning

2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 2021
In this work, we applied deep reinforcement learning for a simulated quadrotor to fly autonomously from a fixed start point to an arbitrary set of goals. The agent was trained using only two random goal positions and the generalization was tested using more than two goal points.
Sondos W. A. Mohamed, Faisal A. A. Mohamed, Ahmed M. O. Mohamed   +2 more
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Deep Reinforcement Learning

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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Deep Reinforcement Learning: A Survey

IEEE Transactions on Neural Networks and Learning Systems
Deep reinforcement learning (DRL) integrates the feature representation ability of deep learning with the decision-making ability of reinforcement learning so that it can achieve powerful end-to-end learning control capabilities. In the past decade, DRL has made substantial advances in many tasks that require perceiving high-dimensional input and ...
Xu Wang, Sen Wang, Xingxing Liang, Dawei Zhao, Jincai Huang, Xin Xu, Bin Dai, Qiguang Miao   +7 more
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Implementation of Deep Reinforcement Learning

Proceedings of the 2019 2nd International Conference on Information Science and Systems, 2019
Reinforcement Learning (RL) is different from supervised learning, which is learning from a training set of labeled examples provided by a knowledgable external supervisor. RL is also different from unsupervised learning, which is typically about finding structure hidden in collections of unlabeled data.
Shao-I Chu, Yu-Jung Huang, Meng-Jhe Li, An-Hong Li   +3 more
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From Reinforcement Learning to Deep Reinforcement Learning: An Overview

2018
This article provides a brief overview of reinforcement learning, from its origins to current research trends, including deep reinforcement learning, with an emphasis on first principles.
Pierre Baldi, Sameer Singh, Guillaume Hocquet, Forest Agostinelli   +3 more
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Reinforcement and Deep Reinforcement Machine Learning

2017
Data-driven learning is a very strong concept. This concept is chased and converted into wonderful applications. Whole stream of Data Engineering and Data Sciences emerged out of that. The data is collected from various sources. It is collected from big hospitals, data repositories, from cookies running in your machines, intelligent applications ...
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Deep reinforcement learning for scheduling

2018
Mocanu, E., Gibescu, M., Nguyen, H.P.
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