Results 51 to 60 of about 1,056,132 (188)

Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates [PDF]

IEEE International Conference on Robotics and Automation, 2016
Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning ...
S. Gu, E. Holly, T. Lillicrap, S. Levine
semanticscholar   +1 more source

Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems

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, Lulin Zhao, Tao Yu, Xiaoshun Zhang   +3 more
doaj   +1 more source

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, Jinying Lin, Qixin Sha, Bo He, Guangliang Li   +4 more
doaj   +1 more source

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning [PDF]

IEEE International Conference on Robotics and Automation, 2017
Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance.
Anusha Nagabandi, G. Kahn, R. Fearing, S. Levine   +3 more
semanticscholar   +1 more source

Reinforcement Learning with A* and a Deep Heuristic

CoRR, 2018
A* is a popular path-finding algorithm, but it can only be applied to those domains where a good heuristic function is known. Inspired by recent methods combining Deep Neural Networks (DNNs) and trees, this study demonstrates how to train a heuristic represented by a DNN and combine it with A*.
Ariel Keselman, Sergey Ten, Adham Ghazali, Majed Jubeh   +3 more
openaire   +2 more sources

An Inverse Kinematics Solution for a Series-Parallel Hybrid Banana-Harvesting Robot Based on Deep Reinforcement Learning

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, Peichen Huang, Minglong Wang, Yao Xu, Rihong Zhang, Lixue Zhu   +5 more
doaj   +1 more source

Deep Reinforcement Learning

CoRR, 2018
Under review for Morgan & Claypool: Synthesis Lectures in Artificial Intelligence and Machine ...
openaire   +2 more sources

Kickstarting Deep Reinforcement Learning

CoRR, 2018
We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their
Simon Schmitt, Jonathan J. Hudson, Augustin Zídek, Simon Osindero, Carl Doersch, Wojciech M. Czarnecki, Joel Z. Leibo, Heinrich Küttler, Andrew Zisserman, Karen Simonyan, S. M. Ali Eslami   +10 more
openaire   +2 more sources

Body Language Analysis Based on Deep Reinforcement Learning [PDF]

ITM Web of Conferences
Deep 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
doaj   +1 more source

Deep Reinforcement Learning for NLP [PDF]

Proceedings of ACL 2018, Tutorial Abstracts, 2018
Many Natural Language Processing (NLP) tasks (including generation, language grounding, reasoning, information extraction, coreference resolution, and dialog) can be formulated as deep reinforcement learning (DRL) problems. However, since language is often discrete and the space for all sentences is infinite, there are many challenges for formulating ...
William Yang Wang, Jiwei Li 0001, Xiaodong He 0001   +2 more
openaire   +1 more source