Introduction to Reinforcement Learning - Distributed RL Systems - Lecture 9

Introduction to Reinforcement Learning - Distributed RL Systems - Lecture 9

Explore the ninth lecture in a course on reinforcement learning, focusing on distributed systems in RL. Delve into the foundations of system architecture, properties of distributed systems, and various approaches to updating model parameters. Examine case studies including MapReduce, DisBeliefF, and AlexNet, and learn about the development of distributed RL systems from Deep Q Network to modern implementations like A3C and IMPALA. Investigate parallelizable algorithms, system designs for AI in modern games like AlphaGo Zero and AlphaStar, and gain insights into the evolution of distributed reinforcement learning architectures.