Faster fitted q-iteration using zero-suppressed decision diagram

This invention describes a computer method to speed up a machine learning technique called Fitted Q-iteration, which helps computers learn optimal actions in different situations. It achieves this by using a specialized data structure called a Zero-Suppressed Decision Diagram (ZDD) to efficiently represent and process complex data about possible future states and actions, allowing the learning process to be faster.

Why it matters: The increasing scale and complexity of reinforcement learning applications in areas like robotics and autonomous systems have amplified the need for more efficient algorithms, making speed improvements like this more critical than in 2021.

Status: Under examinationA pending application. It has not been granted, and it has not been abandoned.
How hard to build: SpecializedSoftware logic, ZDDs, machine learning algorithms

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