Learn what multi-agent reinforcement learning is and some of the challenges it faces and overcomes.
You will also learn what an agent is and how multi-agent systems can be both cooperative and adversarial. Be walked through a grid world example to highlight some of the benefits of both decentralized and centralized reinforcement learning architectures.
Watch our full video series about Reinforcement Learning: https://youtube.com/playlist?list=PLn8PRpmsu08qw_IwpgVNsKiJQpvvW0MmM
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:
- Try MATLAB Example: Train Multiple Agents for Area Coverage: https://bit.ly/3Ix1Kf7
- Read paper: Multi-agent reinforcement learning: An overview: https://bit.ly/3nVrNmN
- Read paper: Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents: https://bit.ly/3nVK7My
- Read paper: Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning: https://bit.ly/3v7LxaT
Check out the individual videos in the series:
• What Is Reinforcement Learning?: https://youtu.be/pc-H4vyg2L4
• Understanding the Environment and Rewards: https://youtu.be/0ODB_DvMiDI
• Policies and Learning Algorithms: https://youtu.be/7cF3VzP5EDI
• The Walking Robot Problem: https://youtu.be/Wypc1a-1ZYA
• Overcoming the Practical Challenges: https://youtu.be/zHV3UcH-nr0
• An Introduction to Multi-Agent Reinforcement Learning: https://youtu.be/qgb0gyrpiGk
• Why Choose Model-Based Reinforcement Learning?: https://youtu.be/ztT2ZLWTfXw
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