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NTIS 바로가기Robotics and autonomous systems, v.135, 2021년, pp.103671 -
Ramezani Dooraki, Amir (Corresponding authors.) , Lee, Deok-Jin
Abstract Animals learn to master their capabilities by trial and error, and with out having any knowledge about their dynamics model and mathematical or physical rules. They use their maximum capabilities in an optimized way. This is the result of millions of years of evolution where the best of di...
Floreano 2008 Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Sutton 1998 Introduction to reinforcement learning
Dooraki 2018 Experience, imitation and reflection; confucius’ conjecture and machine learning
Nature Mnih 518 7540 529 2015 10.1038/nature14236 Human-level control through deep reinforcement learning
Nature Silver 529 7587 484 2016 10.1038/nature16961 Mastering the game of go with deep neural networks and tree search
Silver 2017 Mastering chess and shogi by self-play with a general reinforcement learning algorithm
stockfishchess.org, Stockfish Chess Engine, URL: http://stockfishchess.org/.
AlphaZero-Stockfish, AlphaZero vs Stockfish, URL: https://www.chess.com/news/view/updated-alphazero-crushes-stockfish-in-new-1-000-game-match.
Forestier 2016 The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS 2016) Autonomous exploration, active learning and human guidance with open-source poppy humanoid robot platform and explauto library
Artificial Intelligence Kompella 247 Supplement C 313 2017 10.1016/j.artint.2015.02.001 Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots
Sensors Ramezani Dooraki 18 10 2018 An end-to-end deep reinforcement learning-based intelligent agent capable of autonomous exploration in unknown environments
Lillicrap 2015 Continuous control with deep reinforcement learning
Schulman 2015 Trust region policy optimization
Schulman 2017 Proximal policy optimization algorithms
Dooraki 118 2019 2019 16th International Conference on Ubiquitous Robots (UR) Multi-rotor robot learning to fly in a bio-inspired way using reinforcement learning
Sadeghi 2016 (Cad)$?2$rl: Real single-image flight without a single real image
Zhang 2015 Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Ng 363 2006 Experimental Robotics IX Autonomous inverted helicopter flight via reinforcement learning
Abbeel 1 2006 Proceedings of the 19th International Conference on Neural Information Processing Systems An application of reinforcement learning to aerobatic helicopter flight
Gazebosim.org, Gazebo Simulator, URL: http://gazebosim.org/.
Furrer 595 2016 Robot Operating System (ROS): The Complete Reference (Volume 1)
IEEE Robot. Autom. Lett. Hwangbo 2 4 2096 2017 10.1109/LRA.2017.2720851 Control of a quadrotor with reinforcement learning
Lambert 2019 Low level control of a quadrotor with deep model-based reinforcement learning
ACM Trans. Cyber-Phys. Syst. Koch 3 2 22:1 2019 10.1145/3301273 Reinforcement learning for UAV attitude control
Kakade 267 2002 Proceedings of the Nineteenth International Conference on Machine Learning Approximately optimal approximate reinforcement learning
Mnih 1928 2016 Proceedings of the 33rd International Conference on Machine Learning Asynchronous methods for deep reinforcement learning
Bengio 41 2009 Proceedings of the 26th Annual International Conference on Machine Learning Curriculum learning
R.O.S.org, ROS.org - Powering the world’s robots, URL: http://www.ros.org/.
Dhariwal 2017 Openai baselines
M. Kamel, T. Stastny, K. Alexis, R. Siegwart, Model predictive control for trajectory tracking of unmanned aerial vehicles using robot operating system, in: Koubaa, A. (Ed.), Robot Operating System (ROS) the Complete Reference, Volume 2, Springer.
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