Deep Reinforcement Learning for Walking Robots - MATLAB and Simulink Robotics Arena
Sebastian Castro demonstrates an example of controlling humanoid robot locomotion using deep reinforcement learning, specifically the Deep Deterministic Policy Gradient (DDPG) algorithm. The robot is simulated using Simscape Multibody™, while training the control policy is done using Reinforcement Learning Toolbox™.
In this video, Sebastian outlines the setup, training, and evaluation of reinforcement learning with Simulink® models. First, he introduces how to choose states, actions, and a reward function for the reinforcement learning problem. Then he describes the neural network structure and training algorithm parameters. Finally, he shows some training results and discusses the benefits and drawbacks of reinforcement learning.
You can find the example models used in this video in the MATLAB Central File Exchange: http://bit.ly/2HBxe79
For more information, you can access the following resources:
- Reinforcement Learning Tech Talks: http://bit.ly/2HBzMlS
- Blog and Videos: Walking Robot Modeling and Simulation: http://bit.ly/2GV4vL8
- Paper: Continuous Control with Deep Reinforcement Learning: http://bit.ly/2HAkJsp
- Paper: Emergence of Locomotion Behaviours in Rich Environments: http://bit.ly/2HBuTsO
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Learn more about MATLAB: https://goo.gl/8QV7ZZ
Learn more about Simulink: https://goo.gl/nqnbLe
See What's new in MATLAB and Simulink: https://goo.gl/pgGtod
© 2019 The MathWorks, Inc. MATLAB and Simulink are registered
trademarks of The MathWorks, Inc.
See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Видео Deep Reinforcement Learning for Walking Robots - MATLAB and Simulink Robotics Arena канала MATLAB
In this video, Sebastian outlines the setup, training, and evaluation of reinforcement learning with Simulink® models. First, he introduces how to choose states, actions, and a reward function for the reinforcement learning problem. Then he describes the neural network structure and training algorithm parameters. Finally, he shows some training results and discusses the benefits and drawbacks of reinforcement learning.
You can find the example models used in this video in the MATLAB Central File Exchange: http://bit.ly/2HBxe79
For more information, you can access the following resources:
- Reinforcement Learning Tech Talks: http://bit.ly/2HBzMlS
- Blog and Videos: Walking Robot Modeling and Simulation: http://bit.ly/2GV4vL8
- Paper: Continuous Control with Deep Reinforcement Learning: http://bit.ly/2HAkJsp
- Paper: Emergence of Locomotion Behaviours in Rich Environments: http://bit.ly/2HBuTsO
--------------------------------------------------------------------------------------------------------
Get a free product Trial: https://goo.gl/ZHFb5u
Learn more about MATLAB: https://goo.gl/8QV7ZZ
Learn more about Simulink: https://goo.gl/nqnbLe
See What's new in MATLAB and Simulink: https://goo.gl/pgGtod
© 2019 The MathWorks, Inc. MATLAB and Simulink are registered
trademarks of The MathWorks, Inc.
See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Видео Deep Reinforcement Learning for Walking Robots - MATLAB and Simulink Robotics Arena канала MATLAB
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