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Introduction to Machine Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement learning is a type of machine learning in which an agent learns to behave in an environment, by performing certain actions and receiving rewards or penalties. The agent tries to maximize the rewards it receives by choosing the best actions in each situation. Reinforcement learning is different from supervised and unsupervised learning, in which the agent is given input-output pairs or data to learn from.

Markov Decision Process

Reinforcement learning can be formalized as a Markov Decision Process (MDP), which consists of a set of states, actions, rewards, and a transition function. The agent interacts with the environment by selecting actions, and the environment responds by transitioning to a new state and providing a reward. The goal of the agent is to learn a policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time.


One of the most famous examples of reinforcement learning is AlphaGo, a computer program that plays the board game Go. AlphaGo was developed by DeepMind, a research company owned by Google. AlphaGo learned to play Go by playing against itself and other human players, and by receiving feedback on its moves. In 2016, AlphaGo defeated the world champion Lee Sedol in a five-game match.

Another example of reinforcement learning is self-driving cars. Self-driving cars learn to drive by observing human drivers and by receiving feedback on their actions. They also use sensors and cameras to perceive the environment and make decisions based on that information.

Applications and Challenges

Reinforcement learning has many applications in robotics, finance, gaming, and other areas. It can be used to optimize processes, control systems, and make predictions. However, it also has some challenges, such as the exploration-exploitation trade-off, the credit assignment problem, and the curse of dimensionality.

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