Alternatives to Reinforcement Learning for Real-World Problems
Explore alternatives to Reinforcement Learning for real-world problems in this 42-minute conference talk from Open Data Science. Delve into the limitations of Reinforcement Learning in practical applications, focusing on the challenges of simulation and full observability. Discover two related approaches for agent-based learning: Contextual Bandits and Imitation Learning. Learn how these methods simplify the full Reinforcement Learning problem, their formal definitions, differences, limitations, and real-world applications. Gain insights into tools like Microsoft Azure and AWS SageMaker, and understand when to use each approach. Examine concepts such as behavioral cloning, expert systems, and interactive experts. Explore the scalability concerns, data capture methods, and the exciting potential of combining Imitation Learning with Reinforcement Learning. Conclude with a discussion on Offline RL and its significance in addressing real-world challenges.