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Дата на основаване август 5, 1910
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Сектори Ремонт, Сервиз, Поддръжка
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MIT Researchers Develop an Effective Way to Train more Reliable AI Agents
Fields varying from robotics to medication to government are trying to train AI systems to make meaningful decisions of all kinds. For example, using an AI system to wisely control traffic in a busy city might help drivers reach their locations faster, while enhancing security or sustainability.
Unfortunately, teaching an AI system to make excellent choices is no easy job.
Reinforcement knowing models, which underlie these AI decision-making systems, still often stop working when faced with even little variations in the jobs they are trained to perform. In the case of traffic, a design may have a hard time to control a set of crossways with different speed limitations, varieties of lanes, or traffic patterns.
To improve the dependability of support learning models for complicated jobs with variability, MIT researchers have actually introduced a more effective algorithm for them.
The algorithm strategically picks the best jobs for training an AI representative so it can successfully carry out all tasks in a collection of related tasks. In the case of traffic signal control, each job might be one intersection in a job space that consists of all crossways in the city.
By concentrating on a smaller variety of intersections that contribute the most to the algorithm’s general efficiency, this approach takes full advantage of performance while keeping the training cost low.
The scientists discovered that their method was in between 5 and 50 times more efficient than standard techniques on a variety of simulated tasks. This gain in performance assists the algorithm discover a better option in a much faster way, ultimately improving the efficiency of the AI agent.
„We were able to see unbelievable performance enhancements, with an extremely easy algorithm, by thinking outside package. An algorithm that is not very complex stands a much better chance of being embraced by the neighborhood due to the fact that it is easier to carry out and simpler for others to understand,“ says senior author Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS), and a member of the Laboratory for Information and Decision Systems (LIDS).
She is joined on the paper by lead author Jung-Hoon Cho, a CEE college student; Vindula Jayawardana, a graduate trainee in the Department of Electrical Engineering and Computer Technology (EECS); and Sirui Li, an IDSS college student. The research study will exist at the Conference on Neural Information Processing Systems.
Finding a middle ground
To train an algorithm to manage traffic control at many intersections in a city, an engineer would usually select between two primary approaches. She can train one algorithm for each intersection independently, using only that crossway’s information, or train a bigger algorithm utilizing information from all intersections and after that apply it to each one.
But each technique features its share of downsides. Training a different algorithm for each job (such as a provided crossway) is a lengthy process that requires an enormous quantity of information and calculation, while training one algorithm for all jobs often causes below average performance.
Wu and her collaborators looked for a sweet spot between these two techniques.
For their method, they pick a subset of tasks and train one algorithm for each task separately. Importantly, they strategically choose specific jobs which are probably to improve the algorithm’s overall performance on all tasks.
They take advantage of a typical trick from the support learning field called zero-shot transfer knowing, in which an already trained design is applied to a brand-new job without being more trained. With transfer knowing, the model often performs incredibly well on the brand-new neighbor job.
„We understand it would be ideal to train on all the jobs, but we questioned if we might get away with training on a subset of those tasks, apply the outcome to all the jobs, and still see a performance boost,“ Wu states.
To determine which tasks they need to select to take full advantage of anticipated efficiency, the scientists established an algorithm called Model-Based Transfer Learning (MBTL).
The MBTL algorithm has two pieces. For one, it designs how well each algorithm would perform if it were trained individually on one task. Then it models just how much each algorithm’s efficiency would break down if it were transferred to each other job, a concept called generalization performance.
Explicitly modeling generalization efficiency enables MBTL to estimate the value of training on a brand-new job.
MBTL does this sequentially, picking the task which results in the greatest performance gain initially, then choosing additional jobs that supply the most significant subsequent limited enhancements to general efficiency.
Since MBTL just focuses on the most promising tasks, it can significantly enhance the performance of the training procedure.
Reducing training expenses
When the researchers checked this method on simulated jobs, including managing traffic signals, handling real-time speed advisories, and performing numerous timeless control tasks, it was 5 to 50 times more effective than other methods.
This indicates they could reach the same service by training on far less data. For example, with a 50x effectiveness boost, the MBTL algorithm might train on just 2 jobs and attain the same performance as a standard method which utilizes data from 100 jobs.
„From the perspective of the two primary methods, that indicates data from the other 98 jobs was not necessary or that training on all 100 jobs is confusing to the algorithm, so the performance ends up worse than ours,“ Wu says.
With MBTL, adding even a percentage of extra training time could lead to much better performance.
In the future, the researchers plan to create MBTL algorithms that can encompass more complicated problems, such as high-dimensional job spaces. They are likewise thinking about applying their approach to real-world problems, particularly in next-generation mobility systems.