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Andrew Barto

As seen in: Medium, Communications of the ACM, Science Magazine, HackerNoon, Towards Data Science, KDnuggets
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Articles

Rediscovering Reinforcement Learning

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By Andrew Barto
| Communications of the ACM Verified
Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time while interacting with its environment. What we now call RL has roots in animal learning theories from psychology, and it played key roles in some of the earliest artificial intelligence (AI) systems.
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Federal Funding of Academic Research

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By Andrew Barto
| buff.ly
ACM encourages its members to take a direct hand in shaping the future of the association. There are more ways than ever to get involved. Get Involved By opening CACM to the world, we hope to increase engagement among the broader computer science community and encourage non-members to discover the rich resources ACM has to offer. Learn More
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Reinforcement Learning, Part 7: Introduction to Value-Function Approximation

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By Richard Sutton, Andrew Barto, Vyacheslav Efimov
| Towards Data Science
Scaling reinforcement learning from tabular methods to large spaces Reinforcement learning is a domain in machine learning that introduces the concept of an agent learning optimal strategies in complex environments. The agent learns from its actions, which result in rewards, based on the environment’s state. Reinforcement learning is a challenging topic and differs significantly from other areas of machine learning.
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