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A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning

Intelligent Service Robotics, 2021
This article is about deep learning (DL) and deep reinforcement learning (DRL) works applied to robotics. Both tools have been shown to be successful in delivering data-driven solutions for robotics tasks, as well as providing a natural way to develop an end-to-end pipeline from the robot’s sensing to its actuation, passing through the generation of a ...
Eduardo F. Morales, Eduardo F. Morales, Marco A. Esquivel-Basaldua, Rafael Murrieta-Cid, Israel Becerra, Israel Becerra   +5 more
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Learning to Drive with Deep Reinforcement Learning [PDF]

2021 13th International Conference on Knowledge and Smart Technology (KST), 2021
Autonomous driving cars are important due to improved safety and fuel efficiency. Various techniques have been described to consider only a single task, for example, recognition, prediction, and planning with supervised learning techniques. Some limitations of previous studies are: (1) human bias from human demonstration; (2) the need for multiple ...
Nut Chukamphaeng, Peter Auer, Kitsuchart Pasupa, Martin Antenreiter   +3 more
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Reinforcement Learning and Deep Reinforcement Learning

2019
In order to better understand state-of-the-art reinforcement learning agent, deep Q-network, a brief review of reinforcement learning and Q-learning are first described. Then recent advances of deep Q-network are presented, and double deep Q-network and dueling deep Q-network that go beyond deep Q-network are also given.
F. Richard Yu, Ying He
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Deep Reinforcement Learning

2021
This chapter starts by covering the basic concepts involved in reinforcement learning and then describes how to solve reinforcement learning tasks by using basic and deep learning-based solutions. It also provides a brief overview of the typical algorithms central to the deep learning-based solutions, namely DQN, DDPG, and A3C.
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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 ...
openaire   +2 more sources

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
openaire   +2 more sources