How do you integrate and leverage CNNs and RNNs for reinforcement learning?
Reinforcement learning (RL) is a branch of machine learning that focuses on learning from trial and error, based on rewards and penalties. RL agents can interact with complex and dynamic environments, such as games, robotics, or self-driving cars. However, to deal with high-dimensional and sequential data, such as images, videos, or natural language, RL agents need to use neural networks that can extract and process relevant features. In this article, you will learn how to integrate and leverage two types of neural networks for RL: convolutional neural networks (CNNs) and recurrent neural networks (RNNs).