nominets

Nominets are a type of algorithm used in machine learning, specifically within the field of reinforcement learning. They are a portmanteau of "nominative" and "network," suggesting their role in learning to name or classify states and actions. Essentially, nominets are designed to map complex environmental states to discrete categories or labels. This abstraction can be beneficial for agents learning in environments with a very large or continuous state space, as it simplifies the problem by reducing the number of distinct situations the agent needs to consider.

The core idea behind nominets is to learn a function that takes a state as input and outputs a class label. This learned classification can then be used by a reinforcement learning algorithm, such as a Q-learning agent. Instead of learning a value function over the entire continuous state space, the agent can learn values for each of the discrete categories produced by the nominet. This can lead to more efficient learning and better generalization, especially when states that are physically different but functionally similar are grouped together. The nominet itself can be implemented using various neural network architectures. The training of the nominet is often intertwined with the reinforcement learning process, allowing the state representation to adapt as the agent learns.