Reinforcement Machine Learning

Reinforcement Machine Learning (RL or ML with reinforcement) is a sophisticated approach to automated learning that hinges on the dynamics of feedback, rewards, and sanctions. The term "reinforcement" comes from the notion of using rewards to foster desirable actions from the agent.

This methodology involves setting a specific target for the machine. The machine navigates problem-solving through a process of trial and error.

In this setup, the algorithm assesses responses from the environment in light of a defined goal. It employs a function that incentivizes actions contributing to goal achievement and discourages counterproductive ones.

This training method is unique in that it doesn't depend on a conventional data set (training set). As such, it stands apart from both supervised ML and unsupervised ML.

Generally, RL algorithms adjust their strategies to maximize reward acquisition and minimize penalties. This process leads to the evolution and refinement of decision-making or classification models.

Key algorithms in the field of reinforcement learning for optimal action selection include:

  • Q-Learning
  • Double Q-Learning
  • Deep Q-Network (DQN)
  • SARSA (State Action Reward State Action)
  • DDPG (Deep Deterministic Policy Gradient)
  • A3C
  • Genetic algorithms

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