Introduction to Multi-Agent Reinforcement Learning
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- Опубліковано 2 сер 2022
- 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: • 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:
- Try MATLAB Example: Train Multiple Agents for Area Coverage: bit.ly/3Ix1Kf7
- Read paper: Multi-agent reinforcement learning: An overview: bit.ly/3nVrNmN
- Read paper: Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents: bit.ly/3nVK7My
- Read paper: Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning: bit.ly/3v7LxaT
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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Hello Brian, You are doing an amazing job. I hope to see a series prepared by you for using graph learning applications using deep reinforcement learning. In addition to your existing accomplishments, this will truly be something amazing.
That was great, Brian. One question- when going from one iteration of reinforcement learning to the next, how does the robot know what change to make? Is the change random and then the robot calculates the total reward points at the end and if this is better than last one, selects this as a better path? Or is there a certain methodology it follows on how to select the change to make?
Hope the question makes sense.
holy shit brian and multiagent and reinforcement learning? count me in
why your shit is holy? which country do you come from?
Never, loose motivation to post something like this.
Great video, thank you for sharing!
12:10 The problem is that, agents may not be optimised if another agent covers a new block. It’s should use the entire coverage map AND cross reference that agents mapping to make sure that even though the whole improved, the parts still need to learn to optimise their improvement.
Excellent explanation, thank you :)
Great video, thanks !
Great video!
@MATLAB, sir I want an Agent should work like variable switching frequency controller, please guide sir
nice video!! but what is the drawbacks of the centralized RL by the way? Its seem to be better and more suitable for multi-agent problem than decentralized.
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you dont respond so why bother