Neural Nets for NLP 2017 - Reinforcement Learning

This course on Reinforcement Learning covers the following learning outcomes and goals: - Understanding the concept of Reinforcement Learning - Exploring Policy Gradient and REINFORCE algorithms - Learning methods to stabilize Reinforcement Learning - Implementing Value-based Reinforcement Learning techniques The course teaches the following individual skills and tools: - Policy Gradient and REINFORCE algorithms - Stabilization techniques for Reinforcement Learning - Value function estimation in Value-based Reinforcement Learning - Balancing exploration and exploitation in Reinforcement Learning The teaching method of the course involves a lecture format with slides and code examples provided for practical implementation. The intended audience for this course includes students and professionals interested in Neural Networks for Natural Language Processing, specifically focusing on Reinforcement Learning techniques.