Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore TF-Agents, the latest reinforcement learning library for TensorFlow, in this 50-minute presentation by Software Engineers Oscar Ramirez and Sergio Guadarrama. Discover why supervised learning is insufficient for certain tasks and learn about the key differences between supervised and reinforcement learning. Gain insights into measuring the reliability of RL algorithms and see a demonstration of playing Breakout faster using TensorFlow. Delve into topics such as learnable policies, policy savers, and available agents. Understand the distributed collection and architecture in TF-Agents, and explore the differences between bandits and reinforcement learning. Learn about A/B testing, multi-armed bandits, and their applications in recommender systems. Finally, get a glimpse of the TF-Agents roadmap and the developer investment behind this powerful library.