Results 61 to 70 of about 1,052,376 (334)
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
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 +3 more
semanticscholar +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 +4 more
doaj +1 more source
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 +5 more
doaj +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 +3 more
openaire +2 more sources
Body Language Analysis Based on Deep Reinforcement Learning [PDF]
ITM Web of ConferencesDeep 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
Applied Artificial Intelligence, 2023 Nowadays, mobile robots are being widely employed in various settings, including factories, homes, and everyday tasks. Achieving successful implementation of autonomous robot movement largely depends on effective route planning.
Jieren Tan
doaj +1 more source
Spectral normalisation for deep reinforcement learning: an optimisation perspective [PDF]
, 2021 Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can recover the performance of these developments not by changing the ...
Berariu, Tudor +5 more
core
Coordination and communication in deep multi-agent reinforcement learning
, 2022 A growing number of real-world control problems require teams of software agents to solve a joint task through cooperation. Such tasks naturally arise whenever human workers are replaced by machines, such as robot arms in manufacturing or autonomous cars
Schroeder de Witt, Christian A
core +1 more source
CoRR, 2018 Under review for Morgan & Claypool: Synthesis Lectures in Artificial Intelligence and Machine ...
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