PathNet is a novel neural network algorithm that enables efficient transfer learning, continual learning, and multitask learning by evolving pathways through a neural network. These pathways determine which parameters are used and updated during backpropagation. PathNet uses a genetic algorithm to evolve pathways, where pathways are evaluated based on their performance on a task. Once a pathway is learned for a task, its parameters are fixed to prevent catastrophic forgetting, allowing the network to reuse knowledge from previous tasks. PathNet has been tested on various tasks, including supervised learning (binary MNIST, CIFAR, SVHN) and reinforcement learning (Atari, Labyrinth), demonstrating positive transfer and improved performance compared to traditional methods. PathNet also enhances the robustness of a parallel asynchronous reinforcement learning algorithm (A3C) to hyperparameter choices. The algorithm uses a modular deep neural network with multiple layers and modules, where pathways evolve to select which modules are active. PathNet's architecture allows for efficient learning by focusing on a subset of parameters and evolving pathways to optimize performance. The algorithm has been shown to outperform traditional methods in transfer learning, with results indicating that PathNet can significantly reduce training time by reusing knowledge from previous tasks. PathNet's ability to evolve pathways and fix parameters after learning makes it suitable for continual learning and multitask learning. The algorithm has been tested on various tasks, including supervised and reinforcement learning, and has shown promising results in improving performance and reducing training time. PathNet's approach to pathway evolution and parameter fixing is similar to progressive neural networks, which also prevent catastrophic forgetting by design. The algorithm's effectiveness has been demonstrated in multiple experiments, showing that PathNet can achieve faster learning and better performance compared to traditional methods. PathNet's ability to evolve pathways and reuse knowledge from previous tasks makes it a promising approach for neural network training.PathNet is a novel neural network algorithm that enables efficient transfer learning, continual learning, and multitask learning by evolving pathways through a neural network. These pathways determine which parameters are used and updated during backpropagation. PathNet uses a genetic algorithm to evolve pathways, where pathways are evaluated based on their performance on a task. Once a pathway is learned for a task, its parameters are fixed to prevent catastrophic forgetting, allowing the network to reuse knowledge from previous tasks. PathNet has been tested on various tasks, including supervised learning (binary MNIST, CIFAR, SVHN) and reinforcement learning (Atari, Labyrinth), demonstrating positive transfer and improved performance compared to traditional methods. PathNet also enhances the robustness of a parallel asynchronous reinforcement learning algorithm (A3C) to hyperparameter choices. The algorithm uses a modular deep neural network with multiple layers and modules, where pathways evolve to select which modules are active. PathNet's architecture allows for efficient learning by focusing on a subset of parameters and evolving pathways to optimize performance. The algorithm has been shown to outperform traditional methods in transfer learning, with results indicating that PathNet can significantly reduce training time by reusing knowledge from previous tasks. PathNet's ability to evolve pathways and fix parameters after learning makes it suitable for continual learning and multitask learning. The algorithm has been tested on various tasks, including supervised and reinforcement learning, and has shown promising results in improving performance and reducing training time. PathNet's approach to pathway evolution and parameter fixing is similar to progressive neural networks, which also prevent catastrophic forgetting by design. The algorithm's effectiveness has been demonstrated in multiple experiments, showing that PathNet can achieve faster learning and better performance compared to traditional methods. PathNet's ability to evolve pathways and reuse knowledge from previous tasks makes it a promising approach for neural network training.