Generalization in Deep Reinforcement Learning
Observations of trained deep reinforcement learning agents give the impression that agents have constructed a generalized representation that supports insightful action decisions. We re examine what is meant by generalization in RL, and propose several definitions and techniques based on an agent's performance in on policy, off policy, and unreachable states. We demonstrate these techniques on a common benchmark task for deep RL, and show that more experimentation and analysis is necessary before claims of generalization can be supported.