{"id":76479,"date":"2025-04-08T00:41:36","date_gmt":"2025-04-08T04:41:36","guid":{"rendered":"https:\/\/world.edu\/?p=76479"},"modified":"2025-04-08T00:41:36","modified_gmt":"2025-04-08T04:41:36","slug":"what-is-reinforcement-learning-an-ai-researcher-explains-a-key-method-of-teaching-machines-and-how-it-relates-to-training-your-dog","status":"publish","type":"post","link":"https:\/\/world.edu\/what-is-reinforcement-learning-an-ai-researcher-explains-a-key-method-of-teaching-machines-and-how-it-relates-to-training-your-dog\/","title":{"rendered":"What is reinforcement learning? An AI researcher explains a key method of teaching machines \u2013 and how it relates to training your dog"},"content":{"rendered":"

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Understanding intelligence and creating intelligent machines are\u00a0grand scientific challenges of our times<\/a>. The ability to\u00a0learn from experience<\/a>\u00a0is a cornerstone of intelligence for machines and living beings alike.<\/p>\n

In a remarkably prescient\u00a01948 report<\/a>,\u00a0Alan Turing<\/a>\u00a0\u2013 the father of modern computer science \u2013 proposed the construction of machines that display intelligent behavior. He also discussed the \u201ceducation\u201d of such machines \u201cby means of rewards and punishments.\u201d<\/p>\n

Turing\u2019s ideas ultimately led to the development of\u00a0reinforcement learning<\/a>, a branch of\u00a0artificial intelligence<\/a>. Reinforcement learning designs intelligent agents by training them to maximize rewards as they interact with their environment.<\/p>\n

As a\u00a0machine learning researcher<\/a>, I find it fitting that reinforcement learning pioneers\u00a0Andrew Barto<\/a>\u00a0and\u00a0Richard Sutton<\/a>\u00a0were awarded the\u00a02024 ACM Turing Award<\/a>.<\/p>\n

What is reinforcement learning?<\/strong><\/h4>\n

Animal trainers know that animal behavior can be influenced by rewarding desirable behaviors. A dog trainer gives the dog a treat when it does a trick correctly. This reinforces the behavior, and the dog is more likely to do the trick correctly the next time. Reinforcement learning borrowed this insight\u00a0from animal psychology<\/a>.<\/p>\n

But reinforcement learning is about training computational agents, not animals. The agent can be a software agent like a chess-playing program. But the agent can also be an embodied entity like a robot learning to do household chores. Similarly, the environment of an agent can be virtual, like the chessboard or the designed world in a video game. But it can also be a house where a robot is working.<\/p>\n

Just like animals, an agent can perceive aspects of its environment and takeactions. A chess-playing agent can access the chessboard configuration and make moves. A robot can sense its surroundings with cameras and microphones. It can use its motors to move about in the physical world.<\/p>\n

Agents also have goals that their human designers program into them. A chess-playing agent\u2019s goal is to win the game. A robot\u2019s goal might be to assist its human owner with household chores.<\/p>\n

The reinforcement learning problem in AI is how to design agents that achieve their goals by perceiving and acting in their environments. Reinforcement learning makes a bold claim: All goals can be achieved by designing a numerical signal, called the reward, and having the agent maximize the total sum of rewards it receives.
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