Goldbach conjecture is one of the most famous open mathematical problems. It states that every even number, bigger than two, can be presented as a sum of 2 prime numbers. In this work we present a ...

In this paper an incremental learning algorithm for function approximation is presented. The algorithm utilizes the current training pattern to generate an approximately learned function with minimum ...

Learn how to balance the complexity and interpretability of your function approximation methods, such as linear, nonlinear, or deep neural networks, in reinforcement learning.

We present a fully implemented instantiation of evolutionary function approximation which combines NEAT, a neuroevolutionary optimization technique, with Q-learning, a popular TD method. The resulting ...

Model-Based Function Approximation for Reinforcement Learning (2007) Nicholas K. Jong and Peter Stone Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in ...

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The function approximation methods which work with unstructured data without using optimization are described in section 2.6. ... Keywords: deep learning, function approximation, manifold learning, ...

This repository contains the implementation of an agent designed to complete an episodic Markov Decision Process (MDP) task within the gymnasium (gym) framework. The agent's objective is to navigate a ...