NeuroEvolveQL: Combining Neuroevolution with Deep-Q Learning
This repository implements a framework for combining NeuroEvolution of Augmenting Topologies (NEAT) with Deep-Q learning to solve complex real-world problems. By combining these two powerful techniques, we aim to achieve:
Evolving Effective Deep-Q Networks: NEAT automatically evolves the architecture of the Deep-Q network, potentially leading to more efficient and effective solutions compared to manually designed architectures.
Tackling Complex Problems: The combination of NEAT's ability to search large solution spaces and Deep-Q learning's effectiveness in handling reinforcement learning problems makes this framework suitable for complex tasks.