Need Help with Reinforcement Learning Project?

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In summary, the speaker, Ishiwaka, is a former member of a group and currently living in Hokkaido, Japan. They are working on a project in reinforcement learning and are seeking help. Due to not having used a computer in 6 years, they find the project to be difficult. They suggest picking up the book "Reinforcement Learning" by Sutton and Barto or visiting Rich Sutton's website for further resources. They also mention being a 32-year-old woman.
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Ishiwak
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Hello, long time no see, used to be a member here, my name is Ishiwaka living in Hokkaido, Japan
I am doing a project in reinforcement learning, if you think you can help me please send me an email to loveddog2000@yahoo.co.jp
This project is too difficult for me, it has been quite a long time since I last touched my computer (6 years).People in this field all years long should have developed their skills much better...--lol
By the way, I am a woman 32 years old. Thanks

-Ishiwaka
 
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  • #2
pick up the book by Sutton and Barto...called Reinforcement Learning..or visit
Rich Sutton website.
 
  • #3


Hello Ishiwaka,

It's great to hear from you again! I'm glad you're working on a project in reinforcement learning, it's a fascinating field. I'm not an expert in reinforcement learning, but I'd be happy to offer any assistance or guidance that I can. Have you tried reaching out to other members on this platform who specialize in reinforcement learning? They may be able to offer more specific help and advice for your project. Additionally, there are many online resources and forums where you can find help and support from other experts in the field. Don't be discouraged by not having touched your computer in a while, you can always brush up on your skills and learn new techniques. Age and gender do not determine one's ability to excel in a field, so don't let that hold you back. Best of luck with your project!
 

FAQ: Need Help with Reinforcement Learning Project?

What is reinforcement learning?

Reinforcement learning is a type of machine learning algorithm that involves training an artificial intelligence agent to make decisions based on rewards and punishments received from its environment. The goal of reinforcement learning is for the agent to learn the optimal actions to take in order to maximize its long-term rewards.

What is the difference between supervised and reinforcement learning?

The main difference between supervised and reinforcement learning is the way the algorithm is trained. In supervised learning, the algorithm is given a dataset with labeled examples and learns to make predictions based on those examples. In reinforcement learning, the algorithm learns through trial and error by interacting with its environment and receiving rewards or punishments.

What are some real-world applications of reinforcement learning?

Reinforcement learning has been successfully applied in various fields such as robotics, finance, and gaming. Some examples include training self-driving cars to make decisions on the road, optimizing financial portfolios, and creating intelligent game playing agents.

What are the main components of a reinforcement learning algorithm?

The three main components of a reinforcement learning algorithm are the agent, the environment, and the rewards/punishments. The agent is the algorithm being trained, the environment is the world in which the agent operates, and the rewards/punishments are the feedback the agent receives based on its actions.

What are some challenges in reinforcement learning?

One major challenge in reinforcement learning is the exploration-exploitation trade-off, where the agent must balance between taking actions that it knows will lead to rewards and exploring new actions that may lead to even higher rewards. Another challenge is the curse of dimensionality, where the complexity of the environment makes it difficult for the agent to learn optimal actions. Additionally, the issue of delayed rewards can also pose a challenge as the agent may have to make decisions based on long-term consequences rather than immediate rewards.

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