MIT researchers develop an efficient way to train more reliable AI agents

The researchers found that their technique was between five and 50 times more efficient than standard approaches on an array of simulated tasks.

By focusing on a smaller number of intersections that contribute the most to the algorithm's overall effectiveness, this method maximizes performance while keeping the training cost low.

Reinforcement learning models, which underlie these AI decision-making systems, still often fail when faced with even small variations in tasks.

An algorithm that is not very complicated stands a better chance of being adopted by the community because it is easier to implement and easier for others to understand.

Read at MIT News | Massachusetts Institute of Technology