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MIT Researchers Develop an Efficient Way to Train more Reliable AI Agents

Fields varying from robotics to medication to government are attempting 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 could help drivers reach their locations quicker, while enhancing security or sustainability.

Unfortunately, teaching an AI system to make excellent choices is no simple job.

Reinforcement learning designs, which underlie these AI decision-making systems, still often fail when confronted with even small variations in the tasks they are trained to perform. When it comes to traffic, a model may struggle to control a set of crossways with different speed limits, varieties of lanes, or traffic patterns.

To boost the reliability of reinforcement learning models for complex jobs with variability, MIT scientists have introduced a more effective algorithm for training them.

The algorithm strategically chooses the very best jobs for training an AI representative so it can efficiently perform all tasks in a collection of associated jobs. In the case of traffic signal control, each task could be one intersection in a job space that includes all crossways in the city.

By concentrating on a smaller sized number of intersections that contribute the most to the algorithm’s overall efficiency, this method takes full advantage of performance while keeping the training cost low.

The researchers found that their method was in between five and 50 times more efficient than basic methods on a range of simulated jobs. This gain in performance assists the algorithm discover a much better solution in a much faster way, ultimately enhancing the efficiency of the AI agent.

“We were able to see extraordinary efficiency improvements, with an extremely easy algorithm, by thinking outside the box. An algorithm that is not very complicated stands a better chance of being adopted by the community since it is easier to implement and much easier for others to understand,” states 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 signed up with on the paper by lead author Jung-Hoon Cho, a CEE college student; Vindula Jayawardana, a college student in the Department of Electrical Engineering and Computer Technology (EECS); and Sirui Li, an IDSS graduate trainee. The research will exist at the Conference on Neural Information Processing Systems.

Finding a middle ground

To train an algorithm to control traffic signal at numerous intersections in a city, an engineer would typically select between 2 primary approaches. She can train one algorithm for each crossway independently, utilizing only that crossway’s data, or train a larger algorithm using data from all intersections and then apply it to each one.

But each approach includes its share of disadvantages. Training a different algorithm for each job (such as a provided crossway) is a time-consuming procedure that needs a massive amount of information and calculation, while training one algorithm for all jobs typically leads to substandard efficiency.

Wu and her collaborators looked for a sweet area in between these 2 techniques.

For their method, they choose a subset of tasks and train one algorithm for each job independently. Importantly, they strategically select specific jobs which are more than likely to improve the algorithm’s overall performance on all jobs.

They leverage a typical technique from the support learning field called zero-shot transfer learning, in which an already trained model is applied to a brand-new job without being more trained. With knowing, the model often carries out remarkably well on the brand-new next-door neighbor job.

“We know 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 increase,” Wu says.

To determine which jobs they must pick to take full advantage of anticipated efficiency, the researchers established an algorithm called Model-Based Transfer Learning (MBTL).

The MBTL algorithm has two pieces. For one, it designs how well each algorithm would carry out if it were trained individually on one task. Then it models how much each algorithm’s performance would deteriorate if it were moved to each other job, an idea called generalization performance.

Explicitly modeling generalization efficiency allows MBTL to estimate the value of training on a new task.

MBTL does this sequentially, choosing the job which leads to the greatest efficiency gain initially, then choosing extra jobs that supply the biggest subsequent minimal enhancements to total performance.

Since MBTL only focuses on the most appealing jobs, it can considerably enhance the effectiveness of the training procedure.

Reducing training costs

When the scientists tested this strategy on simulated tasks, consisting of controlling traffic signals, managing real-time speed advisories, and executing several classic control tasks, it was five to 50 times more efficient than other methods.

This implies they might reach the exact same solution by training on far less information. For instance, with a 50x performance boost, the MBTL algorithm could train on simply two jobs and achieve the same performance as a standard method which utilizes data from 100 tasks.

“From the point of view of the 2 primary methods, that means information from the other 98 tasks was not required or that training on all 100 jobs is puzzling to the algorithm, so the performance winds up worse than ours,” Wu states.

With MBTL, adding even a percentage of extra training time might cause far better efficiency.

In the future, the scientists prepare to develop MBTL algorithms that can extend to more complicated issues, such as high-dimensional task areas. They are also thinking about applying their approach to real-world issues, particularly in next-generation mobility systems.