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How Google Deepmind Reinforcement learning works
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How Google-DeepMind Reinforcement Learning Works?

You ever wonder how autonomous systems (like Robots) take decisions in new environment? Know what is "Reinforcement Learning" and how companies like Google DeepMind use reinforcement learning. Book this live session (with code demo) today.


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Reinforcement Learning 

is a part of machine learning. It is based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent can perceive and interpret its environment, take actions, and learn through trial and error. It's about taking the best possible action or path to gain maximum rewards and minimum punishment through observations in a specific situation.

Below are few applications of Reinforcement learning

  • Autonomous cars - when to change lane, automatic parking, trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways. 
  • Industry Automation - AI agents by Deepmind are being used to cool Google Data Centers. This led to a 40% reduction in energy spending. The centers are now fully controlled with the AI system without the need for human intervention.
  • Trading and Financing - An RL agent can decide on such a task; whether to hold, buy, or sell. IBM for example has a sophisticated reinforcement learning based platform that has the ability to make financial trades. It computes the reward function based on the loss or profit of every financial transaction. 

Apart from above it has applications in gaming, healthcare, language processing etc.

If you want to understand "How Google-DeepMind Reinforcement Learning works?" book this live session.


How Google-DeepMind Reinforcement Learning Works?

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