This video shows how to use the reinforcement learning workflow to get a bipedal robot to walk. It also looks at how to modify the default example to make it look more like how one would set up a traditional control problem by adding a reference signal. It will also consider how a Reinforcement Learning equipped agent can replace parts of a traditional control system rather than an end-to-end design. Finally, we show some of the limitations of this design.
You can find the example model used in this video in the MATLAB Central File Exchange: bit.ly/2HBxe79
Watch our full video series about Reinforcement Learning: • Reinforcement Learning
By the end of this series, you’ll be better prepared to answer questions like:
- What is reinforcement learning and why should I consider it when solving my control problem?
- How do I set up and solve the reinforcement learning problem?
- What are some of the benefits and drawbacks of reinforcement learning compared to a traditional controls approach?
Artificial intelligence, machine learning, deep neural networks. These are terms that can spark your imagination of a future where robots are thinking and evolving creatures.
Check out these other resources:
- Reinforcement Learning by Sutton and Barto: bit.ly/2HAYbb4
- Reinforcement Learning Course by David Silver: • RL Course by David Sil...
- Reinforcement Learning Toolbox: bit.ly/2YjuAYa
- Deep Reinforcement Learning for Walking Robots: • Deep Reinforcement Lea...
Check out the individual videos in the series:
• What Is Reinforcement Learning?: • What Is Reinforcement ...
• Understanding the Environment and Rewards: • Understanding Reinforc...
• Policies and Learning Algorithms: • Reinforcement Learning...
• The Walking Robot Problem: • Solving the Walking Ro...
• Overcoming the Practical Challenges: • Overcoming the Practic...
• An Introduction to Multi-Agent Reinforcement Learning: • Introduction to Multi-...
• Why Choose Model-Based Reinforcement Learning?: • Why Choose Model-Based...
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