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How rewards teach reinforcement learning agents to behave

How rewards teach reinforcement learning agents to behave

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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 ...

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 peopl...

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 err...

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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