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Updated in [August 18th, 2023]
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.
Course Syllabus
Introduction
Markov Decision Process (MDP) Theory
Markov Decision Process - Value Iteration
Markov Decision Process - Policy Iteration
Q Learning Theory
Pathfinding with Q-Learning
Exploration vs. Exploitation Problem
Deep Reinforcement Learning Theory
Course Materials (DOWNLOADS)
