Reinforcement Learning with TensorFlow Agents

Dive into reinforcement learning fundamentals and master the implementation of intelligent agents using TensorFlow Agents in this comprehensive 51-minute tutorial. Begin with an overview of reinforcement learning concepts before exploring TF Agents framework and Deep Q Learning algorithms. Learn to construct a board game application using policy gradient methods, then advance to continuous control systems and the Actor-Learner API. Discover the TF Agents Bandits Library and its practical applications in building recommendation systems. Gain hands-on experience with multiple reinforcement learning approaches, from discrete action spaces in game environments to continuous control problems and multi-armed bandit scenarios for personalized recommendations.

Syllabus

Reinforcement learning overview (Reinforcement learning with TensorFlow Agents)
Introduction to TF Agents and Deep Q Learning (Reinforcement learning with TensorFlow Agents)
Build a board game app with policy gradient (Reinforcement learning with TensorFlow Agents)
Continuous control and Actor-Learner API (Reinforcement learning with TensorFlow Agents)
Introduction to TF Agents Bandits Library (Reinforcement learning with TensorFlow Agents)
Recommendation with TF Agents Bandits Library (Reinforcement learning with TensorFlow Agents)