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Reinforcement Learning

Train robots for safe interactions, play games like chess and Go, and maximize rewards by learning the best actions.
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What is Reinforcement Learning?

Reinforcement Learning is a powerful form of artificial intelligence that mimics the behavior of humans and animals. It is a type of machine learning that enables computer systems to learn from their environment by taking actions and receiving feedback from their environment. The ultimate goal of reinforcement learning is to find the best possible action in a given situation that will maximize rewards and minimize losses.Reinforcement Learning enables machines to learn from their environment by taking actions and seeing the results of their actions. Through trial and error, the machines are able to determine which actions are most likely to produce the desired result, and the resulting actions become the optimal strategy. This process allows machines to learn complex tasks that would otherwise be difficult or impossible to program.Reinforcement Learning is particularly useful in robotics, where machines can learn to interact with their environment in a safe and efficient manner. It also has applications in gaming, where robots can learn to play games such as chess and Go at a high level.

Use Cases And Features

1. Training robots to interact safely with their environment.
2. Developing robots to play complex games such as chess and Go.
3. Automatically learning the best possible action for a given situation to maximize rewards.

Tool Website Engagement

Last Update: 2 months ago

- Disclaimer: Statistics sourced from third-party providers. Accuracy may fluctuate.

Total Monthly Visits: 2B
Bounce Rate: 34%
Visit Duration(avg): 317.86 seconds
Pages Per Visit: 3.95
Country Rank: 840
Global Rank: 24

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Traffic Share By Country

  • United States: 16.58%
  • India: 7.38%
  • Philippines: 3.55%
  • Colombia: 3.45%
  • Canada: 3.10%

Reinforcement Learning - YouTube Reviews


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Amazing, Thats all!

1 Reinforcement Learning Reviews

5.0 out of 5
Ramanujam Kanduri
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"a great tool for Deep Reinforcement Learning Tools and Resources"

What do you like about Reinforcement Learning?
I used Spinning Up in Deep RL and the Comprehensive Review of Deep Reinforcement Learning Methods to gain a deeper understanding of the field. The Godot RL Agents provided a free and open-source tool for deep RL research and game development. The Key Papers in Deep RL offered a list of essential papers to read for research in the field. The IxDRL toolkit provided a novel explainable deep reinforcement learning framework based on analyses of interestingness. The Deep Reinforcement Learning with Plasticity Injection introduced a minimalistic intervention to increase network plasticity without changing the number of trainable parameters.
What did you dislike about Reinforcement Learning?
a lot prerequisite knowledge is needed to understand these tools and actually use them in real time.
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