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I have spent quite a bit of time writing posts on supervised learning and a little time writing about unsupervised learning and semi-supervised learning. Another niche in machine learning, which does not receive a lot of attention, is reinforcement learning.
Reinforcement learning is a topic I am just learning about and it is for that reason that I am studying all I can on this subject so that I can become adept at it.
Reinforcement learning the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximise the total reward.
Although the programmer sets the reward policy–that is, the rules of the game–he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximise the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills. By leveraging the power of search and…
