What is deep reinforcement learning?
Welcome to Answerclub.org
Questions | Answers | Discussions | Knowledge sharing | Communities & more.
Sign Up to our social questions and Answers Engine to ask questions, answer people’s questions, and connect with other people.
Login to our social questions & Answers Engine to ask questions answer people’s questions & connect with other people.
Lost your password? Please enter your email address. You will receive a link and will create a new password via email.
Please briefly explain why you feel this question should be reported.
Please briefly explain why you feel this answer should be reported.
Please briefly explain why you feel this user should be reported.
Questions | Answers | Discussions | Knowledge sharing | Communities & more.
Deep reinforcement learning combines reinforcement learning with deep learning techniques, using neural networks to approximate complex value functions and policies.
Deep reinforcement learning (DRL) is a cutting-edge approach in machine learning that combines deep learning with reinforcement learning principles. It enables agents to learn optimal decision-making policies by interacting with environments and receiving rewards. By leveraging deep neural networks, DRL agents can handle complex tasks and high-dimensional input spaces, achieving remarkable success in domains like gaming, robotics, and autonomous driving. However, training DRL agents can be computationally intensive and requires careful optimization of hyperparameters and network architectures to achieve optimal performance.