Fig 1: Reinforcement learning cycle wherein the agent recursively interacts with its environment and learns by associating rewards with its actions. This agent can be composed of a machine learning model – either entirely, partially, or not at all. The key difference is that those rewards or losses are not obtained from labeled data points but from direct interaction with an environment, be it reality or simulation. In summary, it is the attempt to build an agent that is capable of interpreting its environment and taking an action to maximize its reward.Īt first glance, this sounds similar to supervised learning, where you seek to maximize a reward or minimize a loss as well. Reinforcement learning is an area of machine learning and has become a broad field of study with many different algorithmic frameworks.
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