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Machine Learning With Google

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Machine Learning With Google

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Reinforcement Learning

Reinforcement Learning

In reinforcement learning (RL), a software agent learns through trial and error. When it takes the desired action, the model receives a reward.

Over time, the agent works out how to execute the task to optimize its reward.

The technique can be applied to a vast array of tasks, from controlling autonomous vehicles to improving energy efficiency. But its most celebrated achievements have come in the world of games.


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The AlphaGo Milestone

The AlphaGo Milestone

In March 2016, the Reinforcement Learning technique had a landmark moment. 

A DeepMind system called AlphaGo became the first computer program to defeat a world champion in Go, a famously complex board game.

The victory was reportedly watched by over 200 million people.

AlphaGo learns the game from scratch by playing against different versions of itself thousands of times, incrementally learning through a process of trial and error, known as reinforcement learning. This means it is free to learn the game for itself, unconstrained by orthodox thinking.


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How a Reward Function Works

How a Reward Function Works

In AI systems, the rewards and punishments are calculated mathematically. A self-driving system could receive a -1 when the model hits a wall, and a +1 if it safely passes another car. These signals allow the agent to evaluate its performance.

The algorithm then learns through trial and error to maximize the reward — and ultimately, complete the task in the most desirable manner.


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The Bottom Line

The Bottom Line

There are still major challenges to overcome. RL agents struggle to maximize rewards in complex environments and assess the long-term repercussions of their actions. Nonetheless, the reward-is-enough proponents believe the algorithms’ adaptability could pave a path to AGI.


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