The panel so titled shows the performance of the reinforcement learning algorithm doing dynamic channel allocation in cellular telephones. To learn more about reinforcement learning, please read Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems.

How to Make Software Agents Do the Right Thing: An Introduction to Reinforcement Learning [html][postscript] (to appear in Dr. Dobbs)

Abstract: Deep learning algorithms have recently appeared that pretrain hidden layers of neural networks in unsupervised ways, leading to state-of-the-art performance on large classification problems. These methods can also pretrain networks used for reinforcement learning. However, this ignores the additional information that exists in a reinforcement learning paradigm via the ongoing sequence of state, action, new state tuples. This paper demonstrates that learning a predictive model of state dynamics can result in a pretrained hidden layer structure that reduces the time needed to solve reinforcement learning problems. After training for 0 minutes: Your browser does not support the video tag.

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word reinforcement.''

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Reinforcement learning is an area of machine learning which addresses how an autonomous agent can learn long-term successful behavior through interaction with its environment. The term reinforcement learning has its roots in behavioral psychology, in particular to Pavlovian models of reward learning in animals. The modern theory of reinforcement learning, however, is much more influenced by mathematical theories of optimal control in operations research, such as dynamic programming.

The key distinguishing feature of RL methods is that they learn policies indirectly, by instead learning value functions. RL methods can be constrasted with direct optimization methods, such as genetic algorithms (GA), which attempt to search the policy space directly.

Reinforcement learning is a technique, common in computer science, in which a computer system learns how best to solve some problem through trial-and-error. Classic applications of reinforcement learning involve problems as diverse as robot navigation, network administration and automated surveillance. At the Association for Uncertainty in Artificial Intelligence's annual conference this summer, researchers from MIT's Laboratory for Information and Decision Systems (LIDS) and Computer Science and Artificial Intelligence Laboratory will present a new reinforcement-learning algorithm that, for a wide range of problems, allows computer systems to find solutions much more efficiently than previous algorithms did. The paper also represents the first application of a new programming framework that the researchers developed, which makes it much easier to set up and run reinforcement-learning experiments. Alborz Geramifard, a LIDS postdoc and first author of the new paper, hopes that the software, dubbed RLPy (for reinforcement learning and Python, the programming language it uses), will allow researchers to more efficiently test new algorithms and compare algorithms' performance on different tasks.

Every year there is a brand new reinforcement learning competition. This usually consists of new organizers, and a new website! Instead of replacing the old website every year and breaking hundreds of links, we use a different subdomain each year. So, this page will always exist at: http://rl-competition.org And the specific websites for different years are: NIPS Reinforcement Learning Workshop: Benchmarks and Bakeoffs NIPS Reinforcement Learning Workshop: Benchmarks and Bakeoffs II ICML Reinforcement Learning and Benchmarking Event NIPS Workshop: The First Annual Reinforcement Learning Competition The 2008 Reinforcement Learning Competition:: http://2008.rl-competition.org

RLAI research is research directed toward the long-standing goals of AI (understanding the mind, reproducing human abilities) and is based on reinforcement learning ideas (learning from and while interacting with the world). RLAI research does not include all that is currently thought of as AI research, only that which addresses problems or issues that people regularly encounter in their everyday lives. Similarly, RLAI research does not include research that uses RL methods to solve problems that people do not face and excel at. There is a delimited and fruitful area of research at the confluence of the most ambitious goals of AI and the solution ideas that are arising from RL research.