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A humanoid control strategy based on deep reinforcement learning for enhanced comfort in lower limb rehabilitation robots. [PDF]
Sci RepJin Y +5 more
europepmc +1 more source
Reward design and hyperparameter tuning for generalizable deep reinforcement learning agents in autonomous racing. [PDF]
Sci RepKunda NSS, Kc P, Pandey M, Kumaar AAN.
europepmc +1 more source
A substation robot path planning algorithm based on deep reinforcement learning enhanced by ant colony optimization. [PDF]
Front Robot AIZhang H +5 more
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2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023
Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for complex decision-making tasks. In this paper, we provide an overview of DRL, including its basic components, key algorithms and techniques, and applications in areas s.a.
Sahil Sharma +2 more
semanticscholar +2 more sources
Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for complex decision-making tasks. In this paper, we provide an overview of DRL, including its basic components, key algorithms and techniques, and applications in areas s.a.
Sahil Sharma +2 more
semanticscholar +2 more sources
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 0001 +3 more
openaire +1 more source
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 0001 +3 more
openaire +1 more source
Learning to Drive with Deep Reinforcement Learning
2021 13th International Conference on Knowledge and Smart Technology (KST), 2021Autonomous 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 +3 more
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Deep reinforcement learning: a survey
Frontiers of Information Technology & Electronic Engineering, 2020Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. It has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems.
Haonan Wang +6 more
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