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What is reinforcement learning?

Reinforcement learning (RL) is a type of machine learning where an agent learns to take actions in an environment to maximize a reward signal. The agent receives feedback in the form of a reward or punishment signal based on the actions it takes in the environment. Over time, the agent learns which actions lead to the highest reward and adjusts its behavior accordingly

Here’s a simple example to help illustrate the concept. Let’s say we want to train an agent to play a tic-tac-toe game. The agent would start by randomly placing its moves on the board, and the environment would give it a reward signal based on the game’s outcome. If the agent wins, it will receive a positive reward; if it loses, it will receive a negative reward. The agent would then adjust its strategy based on the feedback and try again in the next game.

As the agent continues playing more games, it learns which strategies lead to the highest reward and which lead to the lowest. Eventually, the agent becomes skilled at playing the game and is able to win against human opponents consistently.

Another example is training a self-driving car. The car would be the agent, and the environment would be the road and other cars on the road. The car would receive a positive reward for successfully reaching its destination and a negative reward for any accidents or traffic violations. By adjusting its behavior based on these rewards, the car learns how to navigate the roads safely and efficiently.

Reinforcement learning is particularly useful in situations where the optimal strategy is not known beforehand or when the environment is constantly changing. It is also commonly used in robotics, game-playing and recommendation systems.

Types of reinforcement learning

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