Reinforcement learning (RL) is often touted as a promising approach for costly and risk-sensitive applications, yet practicing and learning in those domains directly is expensive. It costs time (e.g., OpenAI’s Dota2 project used 10,000 years of experience), it costs money (e.g., “inexpensive” robotic arms used in research typically cost $10,000 to $30,000), and it could even be dangerous to humans. How can an intelligent agent learn to solve tasks in environments in which it cannot practice? For many tasks, such…
