Machine learning: A strategy to learn and understand (Chapter 5)🤖 Part 5: Reinforcement Learning.

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This is last part (Part 5: Reinforcement Learning) of the series Machine learning: A strategy to learn and understand.

Reinforcement learning is an important part of Machine Learning. Reinforcement learning resembles in learning of humans and animals how they learn about the environment. In reinforcement learning, machine learns through its actions performed and results.

Reinforcement learning algorithms with deep learning beats the world champion of game “GO” and human experts in the online multi-player video game “DOTA 2”. Reinforcement learning solves the difficult problem of correlating immediate actions with the delayed returns they produce. Like humans, reinforcement learning algorithms sometimes have to wait a while to see the result of their actions.

In Reinforcement learning, the learner is a decision making agent that takes actions in an environment and receives reward or penalty for its actions in trying to solve a problem. After the trial and run error run, it should learn the best policy, which is the sequence of actions that maximize the total reward.