Reinforcement Learning

Learn about reinforcement learning and the different applications that use reinforcement learning techniques.

Let’s now take a look at why reinforcement learning is such an intriguing topic nowadays. It’s definitely one of the evident research topics these days and is very highly likely to emerge in the coming future.

What is reinforcement learning?

A branch of machine learning called reinforcement learning focuses on teaching agents to make particular choices using their experiences in a dynamic environment. The reinforcement learning paradigm is predicated on the notion that an agent can acquire decision-making skills through interaction with its surroundings. By making decisions that optimize cumulative reward, the agent gains the ability to accomplish a task in an unpredictable and sometimes complicated environment.

The basic components of reinforcement learning include:

  • Agent: The learner or decision-maker that interacts with the environment. It takes actions based on the information it receives and the policy it follows.

  • Environment: The external system with which the agent interacts. It provides feedback to the agent in the form of rewards and influences the state of the environment.

  • Actions: Choices that the agent can make. These actions are usually determined by a policy, which is the strategy that the agent employs to determine its behavior.

  • State: The current situation that the agent finds itself in within the environment. The state provides context for the agent to make decisions.

  • Rewards: Feedback from the environment that the agent seeks to maximize over time. Rewards are used to reinforce or discourage certain behaviors.

The figure below shows the action-reward feedback loop of a generic reinforcement learning model.

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