Artificial Intelligence IV - Reinforcement Learning in Java

Course Overview

Skills and Knowledge:
This course on Reinforcement Learning in Java will provide students with the skills and knowledge to understand and apply Markov Decision Processes, value-iteration and policy-iteration, Q-learning fundamentals, pathfinding algorithms with Q-learning, and Q-learning with neural networks. Students will gain an understanding of the state-of-the-art approach of Q-learning and how to interact with the environment to learn the optimal policy.
Professional Growth:
This course on Reinforcement Learning in Java provides a comprehensive overview of the mathematical background and algorithms used in this field. It covers topics such as Markov Decision Processes, value-iteration and policy-iteration, Q-learning fundamentals, pathfinding algorithms with Q-learning, and Q-learning with neural networks. By taking this course, professionals can gain a better understanding of the concepts and algorithms used in Reinforcement Learning, allowing them to apply these techniques to their own projects and further their professional growth.
Further Education:
This course on Reinforcement Learning in Java is suitable for preparing further education. It covers the mathematical background of reinforcement learning, including Markov Decision Processes, value-iteration, policy-iteration, and Q-learning. It also covers pathfinding algorithms with Q-learning and Q-learning with neural networks. This course provides a comprehensive overview of the fundamentals of reinforcement learning, making it an ideal choice for those looking to further their education in this field.