Deep Reinforcement Learning-based System

A Deep Reinforcement Learning-based System is a deep learning-based system that is a reinforcement learning-based system.

• AKA: End-To-end Reinforcement Learning System, Deep RL System.
• Example(s):
  • Deep Net Reinforcement Learning System,
  • …
• Counter-Example(s):
  • Machine Learning System,
  • Reinforcement Learning System,
  • Deep Learning System.
• See: End-To-End Principle, AlphaGo, Health Care, State Space, Robotics, Video Game, Natural Language Processing, Computer Vision.

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References

2021

• (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Deep_reinforcement_learning Retrieved:2021-7-9.
  • Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g. every pixel rendered to the screen in a video game) and decide what actions to perform to optimize an objective (eg. maximizing the game score). Deep reinforcement learning has been used for a diverse set of applications including but not limited to robotics, video games, natural language processing, computer vision, education, transportation, finance and healthcare.

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