#Reinforcement Learning
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Reinforcement Learning Textbooks
Reinforcement Learning Tutorials
Reinforcement Learning Courses
Reinforcement Learning Lectures
Reinforcement Learning Podcasts
Reinforcement Learning Packages
Reinforcement Learning: An Introduction
by Richard S. Sutton and Andrew G. Barto
"The book consists of three parts. Part I is introductory and problem oriented. We focus on the simplest aspects of reinforcement learning and on its main distinguishing features. One full chapter is devoted to introducing the reinforcement learning problem whose solution we explore in the rest of the book. Part II presents what we see as the three most important elementary solution methods: dynamic programming, simple Monte Carlo methods, and temporal-difference learning. The first of these is a planning method and assumes explicit knowledge of all aspects of a problem, whereas the other two are learning methods. Part III is concerned with generalizing these methods and blending them. Eligibility traces allow unification of Monte Carlo and temporal-difference methods, and function approximation methods such as artificial neural networks extend all the methods so that they can be applied to much larger problems. We bring planning and learning methods together again and relate them to heuristic search. Finally, we summarize our view of the state of reinforcement learning research and briefly present case studies, including some of the most impressive applications of reinforcement learning to date."
Deep Reinforcement Learning: Pong from Pixels
by Andrej Karpathy
Tutorial for training a policy network to play Pong
Other tags: Tutorials in Python
Deep Deterministic Policy Gradients in TensorFlow
by
A Policy Gradient tutorial to solve the Pendulum environment in OpenAI gym
Other tags: Tutorials in Python
Reinforcement Learning
by Georgia Tech
"You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning."
UCL Course on RL
by David Silver
Course on Reinforcement Learning
Learning Machines 101
by Richard M. Golden
The intended audience for this podcast series is the general public and the intended objective of this podcast series is to help popularize and demystify the field of Artificial Intelligence by explaining fundamental concepts in an entertaining manner. However, many advanced topics in artificial intelligence and machine learning will be discussed at a “high-level” so students, scientists, and engineers working in the machine learning area may find this podcast series beneficial for identifying relevant “entry points” into advanced statistical machine learning topics. Relevant references to advanced readings are provided (when applicable) in the show notes for each episode.
Other tags: Machine Learning Podcasts, Deep Learning Podcasts
gym
"OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages."
Other tags: Python Packages
universe
"a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications."
Other tags: Python Packages